Identifying sentiment for a portion of a knowledge base

ABSTRACT

A method includes selecting an entigen group from a knowledge database. The entigen group is a most likely meaning of a phrase of a string of words. An entigen of the entigen group corresponds to a selected identigen from a set of identigens regarding a word of the string of words. The set of identigens represent different meanings of the word. The method further includes identifying other entigen groups of alternate phrases having the most likely meaning from the knowledge database. The alternate phrases are different permutations of the string of words. The method further includes determining a dominant digital representation of a human reaction of the other entigen groups and generating a digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/617,818, entitled “GENERATING AND UTILIZING A KNOWLEDGE BASE,” filed Jan. 16, 2018, which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

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BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computing systems and more particularly to generating data representations of data and analyzing the data utilizing the data representations.

Description of Related Art

It is well known that a massive amount of data is stored in information systems, such as files containing text. It is often difficult to extract useful information from this stored data. There is simply too much data for humans to analyze. While computers may be able to process data faster than humans, computers are challenged when it comes to comprehending concepts or even identifying context that is often helpful to understand the content of the data. Humans understand concepts and context, but cannot process the data like a computer.

It is well known that data is archived in computing systems as a more desirable alternative to pre-computer methods such as paper-document storage. Regardless of the reason why the data is found in the computing systems, the files created to communicate and store the data were created for human understanding, not necessarily for computer understanding. The information of the data can be even more useful if a truest possible meaning of the data can be identified in such a way as to enable sophisticated analysis. Computer systems struggle to understand the data since identifying the truest meaning has not been possible.

Understanding the content of data requires an ability to comprehend words people use to convey their thoughts. If the processing power of computers is to be relevant in the effort to mine massive data files, then a computerized approach must be able to comprehend text, not merely recognize patterns and apply statistical reasoning solutions. The promise of “Big Data” analytics has been difficult since the truest possible meaning of the data cannot be easily determined from massive storage repositories.

Relevant information is masked in an overwhelming volume of words due to ambiguities inherent in words. Increasing processor speed may reduce the challenge presented by data volume, but computer processing to produce the truest possible meaning of the words of the data words is an unmet need. Whether simply searching data files for specific elements of data or extracting specific elements from data for complex analysis, current technology is limited to search-and-retrieve operations driven by word-based pattern matching and statistical modeling. Therefore, even with increased computer processing power, the problem of understanding data still exists.

It is known that one such approach to determine truest meaning is to apply sophisticated pattern matching algorithms to identify similar word patterns in a user's query with those in the data. These algorithms have become quite pervasive, but the basic approach remains that of probabilistic scoring of queries and data indexes to establish the best match between query and data—i.e., statistical reasoning solutions. If a specific answer resides in the database, such as “George Washington was the first President of the United States”, then current software can return that answer in response to the query, “Who was the first President of the United States?” The answer was explicit in the database. If the answer is not explicit in the data, then the matching algorithm will return a list of possible files where the answer might be found. It is then incumbent on a requester to read through the list of potentially relevant documents and find the answer implicit in the data.

A good example of the problem that such a lack of meaning understanding creates is demonstrated in the following; “Who died today?” In this simple three-word query, the requester wants to know the names of the people who died on this day. But a computer is challenged to comprehend this simple question. Instead, the computer algorithm focuses on the key words “died” and “today”. Most algorithms are sufficiently sophisticated to deal with words similar to “died” or its general meaning, such as “death”, “killed”, “passed away”, etc. But more challenging is dealing with the temporal nature of the word “today.” Consequently, if one were to do an internet search with a “search engine”, one would get about over 500,000 responses or hits—a list of links to over 500,000 uniform resource locators (URLs) to sift through. Even if a requester could open, read, and extract useful information from these URLs at the rate of one per second (which is not likely), it would take 150 person-hours (3.75 standard work weeks at 40-hours/week) to get through the entire list. But worse than that, because the computer algorithm did not understand the question, none of the results answer the question. Instead, the word-pattern matching returns a list of URLs that link to deaths: reported in media records with the word “today” in the title (e.g., “New York Today News”), that occurred today in history, or that were reported today, etc. When answering a query requires understanding both the query and the text files, word-based pattern matching is inadequate.

It is further well known that answering queries with current technology relies on the answer being explicit in the text. In order to increase the likelihood that an answer to a potential query will be explicit in the data, one known approach is to seed the data with answers to likely questions. For example, a super computer downloads large data base files and then sets an army of human experts to work reading though the files and preparing a list of question/answer pairs which are loaded into the super computer. When asked a question by a requester, the super computer statistically searches for the highest correlation between the word pattern in the query and the word pattern in the pre-loaded answers. This approach is known as statistical reasoning. Not only is this process of generating question and answer pairs exceedingly expensive and time consuming, it also limits the information that can be extracted from the data to those questions that have been previously created and stored. What is advertised to a layperson as “thinking”, but it is actually no more than retrieving preselected answers from large data files. While such an approach may be adequate for static data sources, such as voluminous government regulations, historical fact tables, or medical diagnostic decision trees, it cannot provide insights into dynamic data (e.g., real time), nor can it leverage the inexhaustible power of a computer's ability to find all relevant, co-related data in huge data files.

It is also well known that many industry leaders are attempting to use deep neural networks to identify objects in photos and recognize the individual words we speak into digital assistants (e.g., consumer voice recognition computer assistance systems). The hope is that this type of artificial intelligence can dramatically improve a machine's ability to grasp the significance of those words by understanding how those words interact to form meaningful sentences. These industry leaders have recognized the importance of comprehending words as an enabler for a wide range of computer functions. But, the neural-net approach still relies on pattern matching and probabilistic scoring and requires some level of supervised learning.

Currently, available technology simply cannot provide insightful knowledge. It can only provide a list of potentially relevant sources to serve as leads for humans to process manually who then generate insightful knowledge, or it can search a database of pre-loaded knowledge generated by experts. But it cannot provide insightful knowledge, or extract all the relevant data for additional analysis from data files because it cannot comprehend the meaning(s) in data, such as text.

As an interesting comparison, humans can read and comprehend text files, but cannot process data fast enough to sort through massive files quickly. Computers can process data quickly, but cannot comprehend the concepts and context conveyed by text as humans do. Either humans need to process at computer speeds or computers need to comprehend at human levels. The technology required to achieve the former is not on the horizon. The technology to achieve the latter has not yet been demonstrated by known approaches.

Therefore, there is useful information that resides in massive data stored in information systems that cannot currently be understood by computers for analysis. The promise of “Big Data” analytics cannot be met if the relevant data cannot be understood and extracted from its massive storage repositories. This current lack of understanding effectively buries relevant information in overwhelming volume and masks it with the ambiguities inherent in words. Increasing processor speed will reduce the challenge presented by data volume, but no currently available software will overcome the challenge presented by words.

Currently, the generalized approach employed by all the technologies and methodologies mentioned above is that the fundamental understanding of natural languages is based on grammar. Specifically, grammar classifies words into five major grammatical types such as functional words, nouns, adjectives, verbs and adverbs. Grammar then uses these grammatical types to study how words should be distributed within a string of words to form a properly constructed grammatical sentence. However, understanding natural languages from a grammatical stance encourages the desertion of two major and crucial points. The first point is that grammar does not reflect the mind's natural ability to learn, create and achieve language and speech. People from all ages and cultures can communicate using natural languages without any formal grammatical training or expertise. The second point is that grammar is not concerned with the words' descriptive purpose or with the things that words are actually trying to describe or identify, but rather with the words' own grammatical operations and purpose (how the word is used within sentences).

This later point forces grammar to divide words that describe single ideas into separate grammatical identifications. For example, grammatically speaking, the word “human” is divided into a noun and an adjective based on how the word “human” is being used within a given sentence. In the sentence “the feelings of a human are profound” the word “human” is a noun, because “human” operates as a noun. But in the sentence “human feelings are profound” the word “human” is now an adjective, because grammatically speaking “human” in this example is operating as an adjective. Another example is what happens to the word “talking”, which grammatically speaking is divided into three different elements such as a noun, an adjective or a verb. In “the talking of the president” the word “talking” is a noun; in “the talking president left the building” the word “talking” is an adjective; and in “the president is talking” the word “talking” is a verb. As a result, grammar not only divided “human” in two diverging terms and “talking” into three completely different terms, but in addition, grammar added complexity, because the grammatical classifications of “human” and “talking” were based on usage of each word within each sentence, not meaning.

But more importantly, grammar ignored the most important aspect behind each word, and that is what every word (human and talking) is actually trying to describe. Descriptively speaking (not grammatically speaking), the word “human” always describes a living being and the word “talking” always describes the same type of action on all sentences above. Another serious limitation that grammar endows is that most colloquial and conversational communications between people are informal and therefore do not follow the rigidness or sophistication demanded by the rules of grammar. This limits grammatical-based technologies from processing this type of data. Consequentially, grammar, aside from adding complexity and unnecessary partitioning of words, also reduces the capacity of computers and software to be flexible to process and understand what people are naturally saying, writing or implying. In view of the foregoing, there is an ongoing need for providing systems and methods of identifying words differently for processing natural languages including creating and maintaining searchable databases that when queried by a user produce results that are precisely and accurately responsive to the user's query. Moreover, because the purpose of ontological categories is to distinguish the elements it studies, failing to properly differentiate such elements from each other leads to serious inconsistencies. Indeed, if we select the wrong parameters to distinguish and study the elements of a given system, the resulting categories could be obtrusive and contradictory. For example, if “motion” is selected as the parameter to study the pieces of a train; then the engine, the passenger cars and the caboose can be confused, because all these pieces experience the same type of motion when the train moves. This is similar to what grammar has done by defining how words are used instead of what words describe. Stating how a word is used in a sentence does not identify what the word is actually trying to describe. This has led grammatical approaches to create obtrusive categories, confusion and contradictions in semantic and meaning-based analysis. For example, in many dictionaries the word “elected” is divided as an adjective and a verb (this is obtrusive within that dictionary); while in other dictionaries, “elected” is only an adjective or only a verb (this is contradictory among dictionaries).

In addition, current technology is focused on identifying the part of speech a word represents as opposed to what the word is describing or intending to describe. To date, this approach or methodology precludes current technology from recognizing the single unique individuals, unique items, or unique things that words represent or are trying to describe in time and space. Nor can current technology assign or associate actions and/or attributes to a unique single individual, item or thing and vice versa. Therefore, there is need in the art for methods and approaches that can analysis big data and address the limitations identified above.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a computing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of various servers of a computing system in accordance with the present invention;

FIG. 3 is a schematic block diagram of an embodiment of various devices of a computing system in accordance with the present invention;

FIGS. 4A and 4B are schematic block diagrams of another embodiment of a computing system in accordance with the present invention;

FIG. 4C is a logic diagram of an embodiment of a method for interpreting content to produce a response to a query within a computing system in accordance with the present invention;

FIG. 5A is a schematic block diagram of an embodiment of a collections module of a computing system in accordance with the present invention;

FIG. 5B is a logic diagram of an embodiment of a method for obtaining content within a computing system in accordance with the present invention;

FIG. 5C is a schematic block diagram of an embodiment of a query module of a computing system in accordance with the present invention;

FIG. 5D is a logic diagram of an embodiment of a method for providing a response to a query within a computing system in accordance with the present invention;

FIG. 5E is a schematic block diagram of an embodiment of an identigen entigen intelligence (IEI) module of a computing system in accordance with the present invention;

FIG. 5F is a logic diagram of an embodiment of a method for analyzing content within a computing system in accordance with the present invention;

FIG. 6A is a schematic block diagram of an embodiment of an element identification module and an interpretation module of a computing system in accordance with the present invention;

FIG. 6B is a logic diagram of an embodiment of a method for interpreting information within a computing system in accordance with the present invention;

FIG. 6C is a schematic block diagram of an embodiment of an answer resolution module of a computing system in accordance with the present invention;

FIG. 6D is a logic diagram of an embodiment of a method for producing an answer within a computing system in accordance with the present invention;

FIG. 7A is an information flow diagram for interpreting information within a computing system in accordance with the present invention;

FIG. 7B is a relationship block diagram illustrating an embodiment of relationships between things and representations of things within a computing system in accordance with the present invention;

FIG. 7C is a diagram of an embodiment of a synonym words table within a computing system in accordance with the present invention;

FIG. 7D is a diagram of an embodiment of a polysemous words table within a computing system in accordance with the present invention;

FIG. 7E is a diagram of an embodiment of transforming words into groupings within a computing system in accordance with the present invention;

FIG. 8A is a data flow diagram for accumulating knowledge within a computing system in accordance with the present invention;

FIG. 8B is a diagram of an embodiment of a groupings table within a computing system in accordance with the present invention;

FIG. 8C is a data flow diagram for answering questions utilizing accumulated knowledge within a computing system in accordance with the present invention;

FIG. 8D is a data flow diagram for answering questions utilizing interference within a computing system in accordance with the present invention;

FIG. 8E is a relationship block diagram illustrating another embodiment of relationships between things and representations of things within a computing system in accordance with the present invention;

FIGS. 8F and 8G are schematic block diagrams of another embodiment of a computing system in accordance with the present invention;

FIG. 8H is a logic diagram of an embodiment of a method for processing content to produce knowledge within a computing system in accordance with the present invention;

FIGS. 8J and 8K are schematic block diagrams another embodiment of a computing system in accordance with the present invention;

FIG. 8L is a logic diagram of an embodiment of a method for generating a query response to a query within a computing system in accordance with the present invention;

FIG. 9A is a schematic block diagram of another embodiment of a computing system in accordance with the present invention;

FIG. 9B is a data flow diagram for answering questions utilizing accumulated knowledge within a computing system in accordance with the present invention;

FIG. 9C is a logic diagram of an embodiment of a method for producing a response to a query within a computing system in accordance with the present invention;

FIG. 10A is a schematic block diagram of another embodiment of a computing system in accordance with the present invention;

FIG. 10B is a data flow diagram for predicting an occurrence utilizing pre-occurrence sequence detection within a computing system in accordance with the present invention;

FIG. 10C is a logic diagram of an embodiment of a method for predicting an occurrence within a computing system in accordance with the present invention;

FIG. 11A is a schematic block diagram of another embodiment of a computing system in accordance with the present invention;

FIG. 11B is a data flow diagram for providing an answer to a question within a computing system in accordance with the present invention;

FIG. 11C is a logic diagram of an embodiment of a method for providing an answer to a question within a computing system in accordance with the present invention;

FIG. 12A is a schematic block diagram of another embodiment of a computing system in accordance with the present invention;

FIG. 12B is a logic diagram of an embodiment of a method for utilizing multiple knowledge bases to produce a query response within a computing system in accordance with the present invention;

FIG. 13A is a data flow diagram for optimizing identification of permutations of element groupings within a computing system in accordance with the present invention;

FIG. 13B is a logic diagram of an embodiment of a method for generating an optimized set of permutations of identified elements within a computing system in accordance with the present invention;

FIG. 14A is a schematic block diagram of another embodiment of a computing system in accordance with the present invention;

FIG. 14B is a data flow diagram for identifying sentiment within a computing system in accordance with the present invention;

FIGS. 14C-E are entigen group diagrams of an embodiment of a knowledge database to illustrate examples of identifying sentiment in accordance with the present invention; and

FIG. 14F is a logic diagram of an embodiment of a method for identifying a digital representation of a human reaction within a computing system in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a computing system 10 that includes a plurality of user devices 12-1 through 12-N, a plurality of wireless user devices 14-1 through 14-N, a plurality of content sources 16-1 through 16-N, a plurality of transactional servers 18-1 through 18-N, a plurality of artificial intelligence (AI) servers 20-1 through 20-N, and a core network 24. The core network 24 includes at least one of the Internet, a public radio access network (RAN), and any private network. Hereafter, the computing system 10 may be interchangeably referred to as a data network, a data communication network, a system, a communication system, and a data communication system. Hereafter, the user device and the wireless user device may be interchangeably referred to as user devices, and each of the transactional servers and the AI servers may be interchangeably referred to as servers.

Each user device, wireless user device, transactional server, and AI server includes a computing device that includes a computing core. In general, a computing device is any electronic device that can communicate data, process data, and/or store data. A further generality of a computing device is that it includes one or more of a central processing unit (CPU), a memory system, a sensor (e.g., internal or external), user input/output interfaces, peripheral device interfaces, communication elements, and an interconnecting bus structure.

As further specific examples, each of the computing devices may be a portable computing device and/or a fixed computing device. A portable computing device may be an embedded controller, a smart sensor, a smart pill, a social networking device, a gaming device, a cell phone, a smart phone, a robot, a personal digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, an engine controller, a vehicular controller, an aircraft controller, a maritime vessel controller, a spacecraft controller, and/or any other portable device that includes a computing core. A fixed computing device may be security camera, a sensor device, a household appliance, a machine, a robot, an embedded controller, a personal computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a camera controller, a video game console, a critical infrastructure controller, and/or any type of home or office computing equipment that includes a computing core. An embodiment of the various servers is discussed in greater detail with reference to FIG. 2. An embodiment of the various devices is discussed in greater detail with reference to FIG. 3.

Each of the content sources 16-1 through 16-N includes any source of content, where the content includes one or more of data files, a data stream, a tech stream, a text file, an audio stream, an audio file, a video stream, a video file, etc. Examples of the content sources include a weather service, a multi-language online dictionary, a fact server, a big data storage system, the Internet, social media systems, an email server, a news server, a schedule server, a traffic monitor, a security camera system, audio monitoring equipment, an information server, a service provider, a data aggregator, and airline traffic server, a shipping and logistics server, a banking server, a financial transaction server, etc. Alternatively, or in addition to, one or more of the various user devices may provide content. For example, a wireless user device may provide content (e.g., issued as a content message) when the wireless user device is able to capture data (e.g., text input, sensor input, etc.).

Generally, an embodiment of this invention presents solutions where the computing system 10 supports the generation and utilization of knowledge extracted from content. For example, the AI servers 20-1 through 20-N ingest content from the content sources 16-1 through 16-N by receiving, via the core network 24 content messages 28-1 through 28-N as AI messages 32-1 through 32-N, extract the knowledge from the ingested content, and interact with the various user devices to utilize the extracted knowledge by facilitating the issuing, via the core network 24, user messages 22-1 through 22-N to the user devices 12-1 through 12-N and wireless signals 26-1 through 26-N to the wireless user devices 14-1 through 14-N.

Each content message 28-1 through 28-N includes a content request (e.g., requesting content related to a topic, content type, content timing, one or more domains, etc.) or a content response, where the content response includes real-time or static content such as one or more of dictionary information, facts, non-facts, weather information, sensor data, news information, blog information, social media content, user daily activity schedules, traffic conditions, community event schedules, school schedules, user schedules airline records, shipping records, logistics records, banking records, census information, global financial history information, etc. Each AI message 32-1 through 32-N includes one or more of content messages, user messages (e.g., a query request, a query response that includes an answer to a query request), and transaction messages (e.g., transaction information, requests and responses related to transactions). Each user message 22-1 through 22-N includes one or more of a query request, a query response, a trigger request, a trigger response, a content collection, control information, software information, configuration information, security information, routing information, addressing information, presence information, analytics information, protocol information, all types of media, sensor data, statistical data, user data, error messages, etc.

When utilizing a wireless signal capability of the core network 24, each of the wireless user devices 14-1 through 14-N encodes/decodes data and/or information messages (e.g., user messages such as user messages 22-1 through 22-N) in accordance with one or more wireless standards for local wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or for wide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite, point-to-point, etc.) to produce wireless signals 26-1 through 26-N. Having encoded/decoded the data and/or information messages, the wireless user devices 14-1 through 14-N and/receive the wireless signals to/from the wireless capability of the core network 24.

As another example of the generation and utilization of knowledge, the transactional servers 18-1 through 18-N communicate, via the core network 24, transaction messages 30-1 through 30-N as further AI messages 32-1 through 32-N to facilitate ingesting of transactional type content (e.g., real-time crypto currency transaction information) and to facilitate handling of utilization of the knowledge by one or more of the transactional servers (e.g., for a transactional function) in addition to the utilization of the knowledge by the various user devices. Each transaction message 30-1 through 30-N includes one or more of a query request, a query response, a trigger request, a trigger response, a content message, and transactional information, where the transactional information may include one or more of consumer purchasing history, crypto currency ledgers, stock market trade information, other investment transaction information, etc.

In another specific example of operation of the generation and utilization of knowledge extracted from the content, the user device 12-1 issues a user message 22-1 to the AI server 20-1, where the user message 22-1 includes a query request and where the query request includes a question related to a first domain of knowledge. The issuing includes generating the user message 22-1 based on the query request (e.g., the question), selecting the AI server 20-1 based on the first domain of knowledge, and sending, via the core network 24, the user message 22-1 as a further AI message 32-1 to the AI server 20-1. Having received the AI message 32-1, the AI server 20-1 analyzes the question within the first domain, generates further knowledge, generates a preliminary answer, generates a quality level indicator of the preliminary answer, and determines to gather further content when the quality level indicator is below a minimum quality threshold level.

When gathering the further content, the AI server 20-1 issues, via the core network 24, a still further AI message 32-1 as a further content message 28-1 to the content source 16-1, where the content message 28-1 includes a content request for more content associated with the first domain of knowledge and in particular the question. Alternatively, or in addition to, the AI server 20-1 issues the content request to another AI server to facilitate a response within a domain associated with the other AI server. Further alternatively, or in addition to, the AI server 20-1 issues the content request to one or more of the various user devices to facilitate a response from a subject matter expert. Having received the content message 28-1, the contents or 16-1 issues, via the core network 24, a still further content message 28-1 to the AI server 20-1 as a yet further AI message 32-1, where the still further content message 28-1 includes requested content. The AI server 20-1 processes the received content to generate further knowledge. Having generated the further knowledge, the AI server 20-1 re-analyzes the question, generates still further knowledge, generates another preliminary answer, generates another quality level indicator of the other preliminary answer, and determines to issue a query response to the user device 12-1 when the quality level indicator is above the minimum quality threshold level. When issuing the query response, the AI server 20-1 generates an AI message 32-1 that includes another user message 22-1, where the other user message 22-1 includes the other preliminary answer as a query response including the answer to the question. Having generated the AI message 32-1, the AI server 20-1 sends, via the core network 24, the AI message 32-1 as the user message 22-1 to the user device 12-1 thus providing the answer to the original question of the query request.

FIG. 2 is a schematic block diagram of an embodiment of the AI servers 20-1 through 20-N and the transactional servers 18-1 through 18-N of the computing system 10 of FIG. 1. The servers may include a computing core 52, one or more visual output devices 74 (e.g., video graphics display, touchscreen, LED, etc.), one or more user input devices 76 (e.g., keypad, keyboard, touchscreen, voice to text, a push button, a microphone, a card reader, a door position switch, a biometric input device, etc.), one or more audio output devices 78 (e.g., speaker(s), headphone jack, a motor, etc.), one or more visual input devices 80 (e.g., a still image camera, a video camera, photocell, etc.), one or more universal serial bus (USB) devices (USB devices 1-U), one or more peripheral devices (e.g., peripheral devices 1-P), one or more memory devices (e.g., one or more flash memory devices 92, one or more hard drive (HD) memories 94, one or more solid state (SS) memory devices 96, and/or cloud memory 98), one or more wireless location modems 84 (e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, time difference of arrival, signal strength, dedicated wireless location, etc.), one or more wireless communication modems 86-1 through 86-N (e.g., a cellular network transceiver, a wireless data network transceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telco interface 102 (e.g., to interface to a public switched telephone network), a wired local area network (LAN) 88 (e.g., optical, electrical), a wired wide area network (WAN) 90 (e.g., optical, electrical), and an energy source 100 (e.g., a battery, a solar power source, a fuel cell, a capacitor, a generator, mains power, backup power, etc.).

The computing core 52 includes a video graphics module 54, one or more processing modules 50-1 through 50-N (e.g., which may include one or more secure co-processors), a memory controller 56, one or more main memories 58-1 through 58-N (e.g., RAM), one or more input/output (I/O) device interfaces 62, an input/output (I/O) controller 60, a peripheral interface 64, one or more USB interfaces 66, one or more network interfaces 72, one or more memory interfaces 70, and/or one or more peripheral device interfaces 68.

The processing modules may be a single processing device or a plurality of processing devices where the processing device may further be referred to as one or more of a “processing circuit”, a “processor”, and/or a “processing unit”. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

Each of the interfaces 62, 66, 68, 70, and 72 includes a combination of hardware (e.g., connectors, wiring, etc.) and may further include operational instructions stored on memory (e.g., driver software) that are executed by one or more of the processing modules 50-1 through 50-N and/or a processing circuit within the interface. Each of the interfaces couples to one or more components of the servers. For example, one of the IO device interfaces 62 couples to an audio output device 78. As another example, one of the memory interfaces 70 couples to flash memory 92 and another one of the memory interfaces 70 couples to cloud memory 98 (e.g., an on-line storage system and/or on-line backup system). In other embodiments, the servers may include more or less devices and modules than shown in this example embodiment of the servers.

FIG. 3 is a schematic block diagram of an embodiment of the various devices of the computing system 10 of FIG. 1, including the user devices 12-1 through 12-N and the wireless user devices 14-1 through 14-N. The various devices include the visual output device 74 of FIG. 2, the user input device 76 of FIG. 2, the audio output device 78 of FIG. 2, the visual input device 80 of FIG. 2, and one or more sensors 82 implemented internally and/or externally to the device (e.g., a still camera, a video camera, servo motors associated with a camera, a position detector, a smoke detector, a gas detector, a motion sensor, an accelerometer, velocity detector, a compass, a gyro, a temperature sensor, a pressure sensor, an altitude sensor, a humidity detector, a moisture detector, an imaging sensor, a biometric sensor, an infrared sensor, an audio sensor, an ultrasonic sensor, a proximity detector, a magnetic field detector, a biomaterial detector, a radiation detector, a weight detector, a density detector, a chemical analysis detector, a fluid flow volume sensor, a DNA reader, a wind speed sensor, a wind direction sensor, an object detection sensor, an object identifier sensor, a motion recognition detector, a battery level detector, a room temperature sensor, a sound detector, a smoke detector, an intrusion detector, a motion detector, a door position sensor, a window position sensor, a sunlight detector, and medical category sensors including: a pulse rate monitor, a heart rhythm monitor, a breathing detector, a blood pressure monitor, a blood glucose level detector, blood type, an electrocardiogram sensor, a body mass detector, an imaging sensor, a microphone, body temperature, etc.).

The various devices further include the computing core 52 of FIG. 2, the one or more universal serial bus (USB) devices (USB devices 1-U) of FIG. 2, the one or more peripheral devices (e.g., peripheral devices 1-P) of FIG. 2, the one or more memories of FIG. 2 (e.g., flash memories 92, HD memories 94, SS memories 96, and/or cloud memories 98), the one or more wireless location modems 84 of FIG. 2, the one or more wireless communication modems 86-1 through 86-N of FIG. 2, the telco interface 102 of FIG. 2, the wired local area network (LAN) 88 of FIG. 2, the wired wide area network (WAN) 90 of FIG. 2, and the energy source 100 of FIG. 2. In other embodiments, the various devices may include more or less internal devices and modules than shown in this example embodiment of the various devices.

FIGS. 4A and 4B are schematic block diagrams of another embodiment of a computing system that includes one or more of the user device 12-1 of FIG. 1, the wireless user device 14-1 of FIG. 1, the content source 16-1 of FIG. 1, the transactional server 18-1 of FIG. 1, the user device 12-2 of FIG. 1, and the AI server 20-1 of FIG. 1. The AI server 20-1 includes the processing module 50-1 (e.g., associated with the servers) of FIG. 2, where the processing module 50-1 includes a collections module 120, an identigen entigen intelligence (IEI) module 122, and a query module 124. Alternatively, the collections module 120, the IEI module 122, and the query module 124 may be implemented by the processing module 50-1 (e.g., associated with the various user devices) of FIG. 3. The computing system functions to interpret content to produce a response to a query.

FIG. 4A illustrates an example of the interpreting of the content to produce the response to the query where the collections module 120 interprets (e.g., based on an interpretation approach such as rules) at least one of a collections request 132 from the query module 124 and a collections request within collections information 130 from the IEI module 122 to produce content request information (e.g., potential sources, content descriptors of desired content). Alternatively, or in addition to, the collections module 120 may facilitate gathering further content based on a plurality of collection requests from a plurality of devices of the computing system 10 of FIG. 1.

The collections request 132 is utilized to facilitate collection of content, where the content may be received in a real-time fashion once or at desired intervals, or in a static fashion from previous discrete time frames. For instance, the query module 124 issues the collections request 132 to facilitate collection of content as a background activity to support a long-term query (e.g., how many domestic airline flights over the next seven days include travelers between the age of 18 and 35 years old). The collections request 132 may include one or more of a requester identifier (ID), a content type (e.g., language, dialect, media type, topic, etc.), a content source indicator, security credentials (e.g., an authorization level, a password, a user ID, parameters utilized for encryption, etc.), a desired content quality level, trigger information (e.g., parameters under which to collect content based on a pre-event, an event (i.e., content quality level reaches a threshold to cause the trigger, trueness), or a timeframe), a desired format, and a desired timing associated with the content.

Having interpreted the collections request 132, the collections module 120 selects a source of content based on the content request information. The selecting includes one or more of identifying one or more potential sources based on the content request information, selecting the source of content from the potential sources utilizing a selection approach (e.g., favorable history, a favorable security level, favorable accessibility, favorable cost, favorable performance, etc.). For example, the collections module 120 selects the content source 16-1 when the content source 16-1 is known to provide a favorable content quality level for a domain associated with the collections request 132.

Having selected the source of content, the collections module 120 issues a content request 126 to the selected source of content. The issuing includes generating the content request 126 based on the content request information for the selected source of content and sending the content request 126 to the selected source of content. The content request 126 may include one or more of a content type indicator, a requester ID, security credentials for content access, and any other information associated with the collections request 132. For example, the collections module 120 sends the content request 126, via the core network 24 of FIG. 1, to the content source 16-1. Alternatively, or in addition to, the collections module 120 may send a similar content request 126 to one or more of the user device 12-1, the wireless user device 14-1, and the transactional server 18-1 to facilitate collecting of further content.

In response to the content request 126, the collections module 120 receives one or more content responses 128. The content response 128 includes one or more of content associated with the content source, a content source identifier, security credential processing information, and any other information pertaining to the desired content. Having received the content response 128, the collections module 120 interprets the received content response 128 to produce collections information 130, where the collections information 130 further includes a collections response from the collections module 120 to the IEI module 122. The collections response includes one or more of transformed content (e.g., completed sentences and paragraphs), timing information associated with the content, a content source ID, and a content quality level. Having generated the collections response of the collections information 130, the collections module 120 sends the collections information 130 to the IEI module 122. Having received the collections information 130 from the collections module 120, the IEI module 122 interprets the further content of the content response to generate further knowledge, where the further knowledge is stored in a memory associated with the IEI module 122 to facilitate subsequent answering of questions posed in received queries.

FIG. 4B further illustrates the example of the interpreting of the content to produce the response to the query where, the query module 124 interprets a received query request 136 from a requester to produce an interpretation of the query request. For example, the query module 124 receives the query request 136 from the user device 12-2, and/or from one or more of the wireless user device 14-2 and the transactional server 18-2. The query request 136 includes one or more of an identifier (ID) associated with the request (e.g., requester ID, ID of an entity to send a response to), a question, question constraints (e.g., within a timeframe, within a geographic area, within a domain of knowledge, etc.), and content associated with the question (e.g., which may be analyzed for new knowledge itself).

The interpreting of the query request 136 includes determining whether to issue a request to the IEI module 122 (e.g., a question, perhaps with content) and/or to issue a request to the collections module 120 (e.g., for further background content). For example, the query module 124 produces the interpretation of the query request to indicate to send the request directly to the IEI module 122 when the question is associated with a simple non-time varying function answer (e.g., question: “how many hydrogen atoms does a molecule of water have?”).

Having interpreted the query request 136, the query module 124 issues at least one of an IEI request as query information 138 to the IEI module 122 (e.g., when receiving a simple new query request) and a collections request 132 to the collections module 120 (e.g., based on two or more query requests 136 requiring more substantive content gathering). The IEI request of the query information 138 includes one or more of an identifier (ID) of the query module 124, an ID of the requester (e.g., the user device 12-2), a question (e.g., with regards to content for analysis, with regards to knowledge minded by the AI server from general content), one or more constraints (e.g., assumptions, restrictions, etc.) associated with the question, content for analysis of the question, and timing information (e.g., a date range for relevance of the question).

Having received the query information 138 that includes the IEI request from the query module 124, the IEI module 122 determines whether a satisfactory response can be generated based on currently available knowledge, including that of the query request 136. The determining includes indicating that the satisfactory response cannot be generated when an estimated quality level of an answer falls below a minimum quality threshold level. When the satisfactory response cannot be generated, the IEI module 122 facilitates collecting more content. The facilitating includes issuing a collections request to the collections module 120 of the AI server 20-1 and/or to another server or user device, and interpreting a subsequent collections response 134 of collections information 130 that includes further content to produce further knowledge to enable a more favorable answer.

When the IEI module 122 indicates that the satisfactory response can be generated, the IEI module 122 issues an IEI response as query information 138 to the query module 124. The IEI response includes one or more of one or more answers, timing relevance of the one or more answers, an estimated quality level of each answer, and one or more assumptions associated with the answer. The issuing includes generating the IEI response based on the collections response 134 of the collections information 130 and the IEI request, and sending the IEI response as the query information 138 to the query module 124. Alternatively, or in addition to, at least some of the further content collected by the collections module 120 is utilized to generate a collections response 134 issued by the collections module 120 to the query module 124. The collections response 134 includes one or more of further content, a content availability indicator (e.g., when, where, required credentials, etc.), a content freshness indicator (e.g., timestamps, predicted time availability), content source identifiers, and a content quality level.

Having received the query information 138 from the IEI module 122, the query module 124 issues a query response 140 to the requester based on the IEI response and/or the collections response 134 directly from the collections module 120, where the collection module 120 generates the collections response 134 based on collected content and the collections request 132. The query response 140 includes one or more of an answer, answer timing, an answer quality level, and answer assumptions.

FIG. 4C is a logic diagram of an embodiment of a method for interpreting content to produce a response to a query within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-3, 4A-4B, and also FIG. 4C. The method includes step 150 where a collections module of a processing module of one or more computing devices (e.g., of one or more servers) interprets a collections request to produce content request information. The interpreting may include one or more of identifying a desired content source, identifying a content type, identifying a content domain, and identifying content timing requirements.

The method continues at step 152 where the collections module selects a source of content based on the content request information. For example, the collections module identifies one or more potential sources based on the content request information and selects the source of content from the potential sources utilizing a selection approach (e.g., based on one or more of favorable history, a favorable security level, favorable accessibility, favorable cost, favorable performance, etc.). The method continues at step 154 where the collections module issues a content request to the selected source of content. The issuing includes generating a content request based on the content request information for the selected source of content and sending the content request to the selected source of content.

The method continues at step 156 where the collections module issues collections information to an identigen entigen intelligence (IEI) module based on a received content response, where the IEI module extracts further knowledge from newly obtained content from the one or more received content responses. For example, the collections module generates the collections information based on newly obtained content from the one or more received content responses of the selected source of content.

The method continues at step 158 where a query module interprets a received query request from a requester to produce an interpretation of the query request. The interpreting may include determining whether to issue a request to the IEI module (e.g., a question) or to issue a request to the collections module to gather further background content. The method continues at step 160 where the query module issues a further collections request. For example, when receiving a new query request, the query module generates a request for the IEI module. As another example, when receiving a plurality of query requests for similar questions, the query module generates a request for the collections module to gather further background content.

The method continues at step 162 where the IEI module determines whether a satisfactory query response can be generated when receiving the request from the query module. For example, the IEI module indicates that the satisfactory query response cannot be generated when an estimated quality level of an answer is below a minimum answer quality threshold level. The method branches to step 166 when the IEI module determines that the satisfactory query response can be generated. The method continues to step 164 when the IEI module determines that the satisfactory query response cannot be generated. When the satisfactory query response cannot be generated, the method continues at step 164 where the IEI module facilitates collecting more content. The method loops back to step 150.

When the satisfactory query response can be generated, the method continues at step 166 where the IEI module issues an IEI response to the query module. The issuing includes generating the IEI response based on the collections response and the IEI request, and sending the IEI response to the query module. The method continues at step 168 where the query module issues a query response to the requester. For example, the query module generates the query response based on the IEI response and/or a collections response from the collections module and sends the query response to the requester, where the collections module generates the collections response based on collected content and the collections request.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 5A is a schematic block diagram of an embodiment of the collections module 120 of FIG. 4A that includes a content acquisition module 180, a content selection module 182, a source selection module 184, a content security module 186, an acquisition timing module 188, a content transformation module 190, and a content quality module 192. Generally, an embodiment of this invention presents solutions where the collections module 120 supports collecting content.

In an example of operation of the collecting of the content, the content acquisition module 180 receives a collections request 132 from a requester. The content acquisition module 180 obtains content selection information 194 based on the collections request 132. The content selection information 194 includes one or more of content requirements, a desired content type indicator, a desired content source identifier, a content type indicator, a candidate source identifier (ID), and a content profile (e.g., a template of typical parameters of the content). For example, the content acquisition module 180 receives the content selection information 194 from the content selection module 182, where the content selection module 182 generates the content selection information 194 based on a content selection information request from the content acquisition module 180 and where the content acquisition module 180 generates the content selection information request based on the collections request 132.

The content acquisition module 180 obtains source selection information 196 based on the collections request 132. The source selection information 196 includes one or more of candidate source identifiers, a content profile, selected sources, source priority levels, and recommended source access timing. For example, the content acquisition module 180 receives the source selection information 196 from the source selection module 184, where the source selection module 184 generates the source selection information 196 based on a source selection information request from the content acquisition module 180 and where the content acquisition module 180 generates the source selection information request based on the collections request 132.

The content acquisition module 180 obtains acquisition timing information 200 based on the collections request 132. The acquisition timing information 200 includes one or more of recommended source access timing, confirmed source access timing, source access testing results, estimated velocity of content update's, content precious, timestamps, predicted time availability, required content acquisition triggers, content acquisition trigger detection indicators, and a duplicative indicator with a pending content request. For example, the content acquisition module 180 receives the acquisition timing information 200 from the acquisition timing module 188, where the acquisition timing module 188 generates the acquisition timing information 200 based on an acquisition timing information request from the content acquisition module 180 and where the content acquisition module 180 generates the acquisition timing information request based on the collections request 132.

Having obtained the content selection information 194, the source selection information 196, and the acquisition timing information 200, the content acquisition module 180 issues a content request 126 to a content source utilizing security information 198 from the content security module 186, where the content acquisition module 180 generates the content request 126 in accordance with the content selection information 194, the source selection information 196, and the acquisition timing information 200. The security information 198 includes one or more of source priority requirements, requester security information, available security procedures, and security credentials for trust and/or encryption. For example, the content acquisition module 180 generates the content request 126 to request a particular content type in accordance with the content selection information 194 and to include security parameters of the security information 198, initiates sending of the content request 126 in accordance with the acquisition timing information 200, and sends the content request 126 to a particular targeted content source in accordance with the source selection information 196.

In response to receiving a content response 128, the content acquisition module 180 determines the quality level of received content extracted from the content response 128. For example, the content acquisition module 180 receives content quality information 204 from the content quality module 192, where the content quality module 192 generates the quality level of the received content based on receiving a content quality request from the content acquisition module 180 and where the content acquisition module 180 generates the content quality request based on content extracted from the content response 128. The content quality information includes one or more of a content reliability threshold range, a content accuracy threshold range, a desired content quality level, a predicted content quality level, and a predicted level of trust.

When the quality level is below a minimum desired quality threshold level, the content acquisition module 180 facilitates acquisition of further content. The facilitating includes issuing another content request 126 to a same content source and/or to another content source to receive and interpret further received content. When the quality level is above the minimum desired quality threshold level, the content acquisition module 180 issues a collections response 134 to the requester. The issuing includes processing the content in accordance with a transformation approach to produce transformed content, generating the collections response 134 to include the transformed content, and sending the collections response 134 to the requester. The processing of the content to produce the transformed content includes receiving content transformation information 202 from the content transformation module 190, where the content transformation module 190 transforms the content in accordance with the transformation approach to produce the transformed content. The content transformation information includes a desired format, available formats, recommended formatting, the received content, transformation instructions, and the transformed content.

FIG. 5B is a logic diagram of an embodiment of a method for obtaining content within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-3, 4A-4C, 5A, and also FIG. 5B. The method includes step 210 where a processing module of one or more processing modules of one or more computing devices of the computing system receives a collections request from the requester. The method continues at step 212 where the processing module determines content selection information. The determining includes interpreting the collections request to identify requirements of the content.

The method continues at step 214 for the processing module determines source selection information. The determining includes interpreting the collections request to identify and select one or more sources for the content to be collected. The method continues at step 216 for the processing module determines acquisition timing information. The determining includes interpreting the collections request to identify timing requirements for the acquisition of the content from the one or more sources. The method continues at step 218 where the processing module issues a content request utilizing security information and in accordance with one or more of the content selection information, the source selection information, and the acquisition timing information. For example, the processing module issues the content request to the one or more sources for the content in accordance with the content requirements, where the sending of the request is in accordance with the acquisition timing information.

The method continues at step 220 for the processing module determines a content quality level for received content area the determining includes receiving the content from the one or more sources, obtaining content quality information for the received content based on a quality analysis of the received content. The method branches to step 224 when the content quality level is favorable and the method continues to step 222 when the quality level is unfavorable. For example, the processing module determines that the content quality level is favorable when the content quality level is equal to or above a minimum quality threshold level and determines that the content quality level is unfavorable when the content quality level is less than the minimum quality threshold level.

When the content quality level is unfavorable, the method continues at step 222 where the processing module facilitates acquisition and further content. For example, the processing module issues further content requests and receives further content for analysis. When the content quality level is favorable, the method continues at step 224 where the processing module issues a collections response to the requester. The issuing includes generating the collections response and sending the collections response to the requester. The generating of the collections response may include transforming the received content into transformed content in accordance with a transformation approach (e.g., reformatting, interpreting absolute meaning and translating into another language in accordance with the absolute meaning, etc.).

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 5C is a schematic block diagram of an embodiment of the query module 124 of FIG. 4A that includes an answer acquisition module 230, a content requirements module 232 a source requirements module 234, a content security module 236, an answer timing module 238, an answer transformation module 240, and an answer quality module 242. Generally, an embodiment of this invention presents solutions where the query module 124 supports responding to a query.

In an example of operation of the responding to the query, the answer acquisition module 230 receives a query request 136 from a requester. The answer acquisition module 230 obtains content requirements information 248 based on the query request 136. The content requirements information 248 includes one or more of content parameters, a desired content type, a desired content source if any, a content type if any, candidate source identifiers, a content profile, and a question of the query request 136. For example, the answer acquisition module 230 receives the content requirements information 248 from the content requirements module 232, where the content requirements module 232 generates the content requirements information 248 based on a content requirements information request from the answer acquisition module 230 and where the answer acquisition module 230 generates the content requirements information request based on the query request 136.

The answer acquisition module 230 obtains source requirements information 250 based on the query request 136. The source requirements information 250 includes one or more of candidate source identifiers, a content profile, a desired source parameter, recommended source parameters, source priority levels, and recommended source access timing. For example, the answer acquisition module 230 receives the source requirements information 250 from the source requirements module 234, where the source requirements module 234 generates the source requirements information 250 based on a source requirements information request from the answer acquisition module 230 and where the answer acquisition module 230 generates the source requirements information request based on the query request 136.

The answer acquisition module 230 obtains answer timing information 254 based on the query request 136. The answer timing information 254 includes one or more of requested answer timing, confirmed answer timing, source access testing results, estimated velocity of content updates, content freshness, timestamps, predicted time available, requested content acquisition trigger, and a content acquisition trigger detected indicator. For example, the answer acquisition module 230 receives the answer timing information 254 from the answer timing module 238, where the answer timing module 238 generates the answer timing information 254 based on an answer timing information request from the answer acquisition module 230 and where the answer acquisition module 230 generates the answer timing information request based on the query request 136.

Having obtained the content requirements information 248, the source requirements information 250, and the answer timing information 254, the answer acquisition module 230 determines whether to issue an IEI request 244 and/or a collections request 132 based on one or more of the content requirements information 248, the source requirements information 250, and the answer timing information 254. For example, the answer acquisition module 230 selects the IEI request 244 when an immediate answer to a simple query request 136 is required and is expected to have a favorable quality level. As another example, the answer acquisition module 230 selects the collections request 132 when a longer-term answer is required as indicated by the answer timing information to before and/or when the query request 136 has an unfavorable quality level.

When issuing the IEI request 244, the answer acquisition module 230 generates the IEI request 244 in accordance with security information 252 received from the content security module 236 and based on one or more of the content requirements information 248, the source requirements information 250, and the answer timing information 254. Having generated the IEI request 244, the answer acquisition module 230 sends the IEI request 244 to at least one IEI module.

When issuing the collections request 132, the answer acquisition module 230 generates the collections request 132 in accordance with the security information 252 received from the content security module 236 and based on one or more of the content requirements information 248, the source requirements information 250, and the answer timing information 254. Having generated the collections request 132, the answer acquisition module 230 sends the collections request 132 to at least one collections module. Alternatively, the answer acquisition module 230 facilitate sending of the collections request 132 to one or more various user devices (e.g., to access a subject matter expert).

The answer acquisition module 230 determines a quality level of a received answer extracted from a collections response 134 and/or an IEI response 246. For example, the answer acquisition module 230 extracts the quality level of the received answer from answer quality information 258 received from the answer quality module 242 in response to an answer quality request from the answer acquisition module 230. When the quality level is unfavorable, the answer acquisition module 230 facilitates obtaining a further answer. The facilitation includes issuing at least one of a further IEI request 244 and a further collections request 132 to generate a further answer for further quality testing. When the quality level is favorable, the answer acquisition module 230 issues a query response 140 to the requester. The issuing includes generating the query response 140 based on answer transformation information 256 received from the answer transformation module 240, where the answer transformation module 240 generates the answer transformation information 256 to include a transformed answer based on receiving the answer from the answer acquisition module 230. The answer transformation information 250 6A further include the question, a desired format of the answer, available formats, recommended formatting, received IEI responses, transformation instructions, and transformed IEI responses into an answer.

FIG. 5D is a logic diagram of an embodiment of a method for providing a response to a query within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-3, 4A-4C, 5C, and also FIG. 5D. The method includes step 270 where a processing module of one or more processing modules of one or more computing devices of the computing system receives a query request (e.g., a question) from a requester. The method continues at step 272 where the processing module determines content requirements information. The determining includes interpreting the query request to produce the content requirements. The method continues at step 274 where the processing module determines source requirements information. The determining includes interpreting the query request to produce the source requirements. The method continues at step 276 where the processing module determines answer timing information. The determining includes interpreting the query request to produce the answer timing information.

The method continues at step 278 the processing module determines whether to issue an IEI request and/or a collections request. For example, the determining includes selecting the IEI request when the answer timing information indicates that a simple one-time answer is appropriate. As another example, the processing module selects the collections request when the answer timing information indicates that the answer is associated with a series of events over an event time frame.

When issuing the IEI request, the method continues at step 280 where the processing module issues the IEI request to an IEI module. The issuing includes generating the IEI request in accordance with security information and based on one or more of the content requirements information, the source requirements information, and the answer timing information.

When issuing the collections request, the method continues at step 282 where the processing module issues the collections request to a collections module. The issuing includes generating the collections request in accordance with the security information and based on one or more of the content requirements information, the source requirements information, and the answer timing information. Alternatively, the processing module issues both the IEI request and the collections request when a satisfactory partial answer may be provided based on a corresponding IEI response and a further more generalized and specific answer may be provided based on a corresponding collections response and associated further IEI response.

The method continues at step 284 where the processing module determines a quality level of a received answer. The determining includes extracting the answer from the collections response and/or the IEI response and interpreting the answer in accordance with one or more of the content requirements information, the source requirements information, the answer timing information, and the query request to produce the quality level. The method branches to step 288 when the quality level is favorable and the method continues to step 286 when the quality level is unfavorable. For example, the processing module indicates that the quality level is favorable when the quality level is equal to or greater than a minimum answer quality threshold level. As another example, the processing module indicates that the quality level is unfavorable when the quality level is less than the minimum answer quality threshold level.

When the quality level is unfavorable, the method continues at step 286 where the processing module obtains a further answer. The obtaining includes at least one of issuing a further IEI request and a further collections request to facilitate obtaining of a further answer for further answer quality level testing as the method loops back to step 270. When the quality level is favorable, the method continues at step 288 where the processing module issues a query response to the requester. The issuing includes transforming the answer into a transformed answer in accordance with an answer transformation approach (e.g., formatting, further interpretations of the virtual question in light of the answer and further knowledge) and sending the transformed answer to the requester as the query response.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 5E is a schematic block diagram of an embodiment of the identigen entigen intelligence (IEI) module 122 of FIG. 4A that includes a content ingestion module 300, an element identification module 302, and interpretation module 304, and answer resolution module 306, and an IEI control module 308. Generally, an embodiment of this invention presents solutions where the IEI module 122 supports interpreting content to produce knowledge that may be utilized to answer questions.

In an example of operation of the producing and utilizing of the knowledge, the content ingestion module 300 generates formatted content 314 based on question content 312 and/or source content 310, where the IEI module 122 receives an IEI request 244 that includes the question content 312 and the IEI module 122 receives a collections response 134 that includes the source content 310. The source content 310 includes content from a source extracted from the collections response 134. The question content 312 includes content extracted from the IEI request 244 (e.g., content paired with a question). The content ingestion module 300 generates the formatted content 314 in accordance with a formatting approach (e.g., creating proper sentences from words of the content). The formatted content 314 includes modified content that is compatible with subsequent element identification (e.g., complete sentences, combinations of words and interpreted sounds and/or inflection cues with temporal associations of words).

The element identification module 302 processes the formatted content 314 based on element rules 318 and an element list 332 to produce identified element information 340. Rules 316 includes the element rules 318 (e.g., match, partial match, language translation, etc.). Lists 330 includes the element list 332 (e.g., element ID, element context ID, element usage ID, words, characters, symbols etc.). The IEI control module 308 may provide the rules 316 and the lists 330 by accessing stored data 360 from a memory associated with the IEI module 122. Generally, an embodiment of this invention presents solutions where the stored data 360 may further include one or more of a descriptive dictionary, categories, representations of element sets, element list, sequence data, pending questions, pending request, recognized elements, unrecognized elements, errors, etc.

The identified element information 340 includes one or more of identifiers of elements identified in the formatted content 314, may include ordering and/or sequencing and grouping information. For example, the element identification module 302 compares elements of the formatted content 314 to known elements of the element list 332 to produce identifiers of the known elements as the identified element information 340 in accordance with the element rules 318. Alternatively, the element identification module 302 outputs un-identified element information 342 to the IEI control module 308, where the un-identified element information 342 includes temporary identifiers for elements not identifiable from the formatted content 314 when compared to the element list 332.

The interpretation module 304 processes the identified element information 340 in accordance with interpretation rules 320 (e.g., potentially valid permutations of various combinations of identified elements), question information 346 (e.g., a question extracted from the IEI request to hundred 44 which may be paired with content associated with the question), and a groupings list 334 (e.g., representations of associated groups of representations of things, a set of element identifiers, valid element usage IDs in accordance with similar, an element context, permutations of sets of identifiers for possible interpretations of a sentence or other) to produce interpreted information 344. The interpreted information 344 includes potentially valid interpretations of combinations of identified elements. Generally, an embodiment of this invention presents solutions where the interpretation module 304 supports producing the interpreted information 344 by considering permutations of the identified element information 340 in accordance with the interpretation rules 320 and the groupings list 334.

The answer resolution module 306 processes the interpreted information 344 based on answer rules 322 (e.g., guidance to extract a desired answer), the question information 346, and inferred question information 352 (e.g., posed by the IEI control module or analysis of general collections of content or refinement of a stated question from a request) to produce preliminary answers 354 and an answer quality level 356. The answer generally lies in the interpreted information 344 as both new content received and knowledge based on groupings list 334 generated based on previously received content. The preliminary answers 354 includes an answer to a stated or inferred question that subject further refinement. The answer quality level 356 includes a determination of a quality level of the preliminary answers 354 based on the answer rules 322. The inferred question information 352 may further be associated with time information 348, where the time information includes one or more of current real-time, a time reference associated with entity submitting a request, and a time reference of a collections response.

When the IEI control module 308 determines that the answer quality level 356 is below an answer quality threshold level, the IEI control module 308 facilitates collecting of further content (e.g., by issuing a collections request 132 and receiving corresponding collections responses 134 for analysis). When the answer quality level 356 compares favorably to the answer quality threshold level, the IEI control module 308 issues an IEI response 246 based on the preliminary answers 354. When receiving training information 358, the IEI control module 308 facilitates updating of one or more of the lists 330 and the rules 316 and stores the updated list 330 and the updated rules 316 in the memories as updated stored data 360.

FIG. 5F is a logic diagram of an embodiment of a method for analyzing content within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-3, 4A-4C, 5E, and also FIG. 5F. The method includes step 370 where a processing module of one or more processing modules of one or more computing devices of the computing system facilitates updating of one or more rules and lists based on one or more of received training information and received content. For example, the processing module updates rules with received rules to produce updated rules and updates element lists with received elements to produce updated element lists. As another example, the processing module interprets the received content to identify a new word for at least temporary inclusion in the updated element list.

The method continues at step 372 where the processing module transforms at least some of the received content into formatted content. For example, the processing module processes the received content in accordance with a transformation approach to produce the formatted content, where the formatted content supports compatibility with subsequent element identification (e.g., typical sentence structures of groups of words).

The method continues at step 374 where the processing module processes the formatted content based on the rules and the lists to produce identified element information and/or an identified element information. For example, the processing module compares the formatted content to element lists to identify a match producing identifiers for identified elements or new identifiers for unidentified elements when there is no match.

The method continues at step 376 with a processing module processes the identified element information based on rules, the lists, and question information to produce interpreted information. For example, the processing module compares the identified element information to associated groups of representations of things to generate potentially valid interpretations of combinations of identified elements.

The method continues at step 378 where the processing module processes the interpreted information based on the rules, the question information, and inferred question information to produce preliminary answers. For example, the processing module matches the interpreted information to one or more answers (e.g., embedded knowledge based on a fact base built from previously received content) with highest correctness likelihood levels that is subject to further refinement.

The method continues at step 380 where the processing module generates an answer quality level based on the preliminary answers, the rules, and the inferred question information. For example, the processing module predicts the answer correctness likelihood level based on the rules, the inferred question information, and the question information. The method branches to step 384 when the answer quality level is favorable and the method continues to step 382 when the answer quality level is unfavorable. For example, the generating of the answer quality level further includes the processing module indicating that the answer quality level is favorable when the answer quality level is greater than or equal to a minimum answer quality threshold level. As another example, the generating of the answer quality level further includes the processing module indicating that the answer quality level is unfavorable when the answer quality level is less than the minimum answer quality threshold level.

When the answer quality level is unfavorable, the method continues at step 382 where the processing module facilitates gathering clarifying information. For example, the processing module issues a collections request to facilitate receiving further content and or request question clarification from a question requester. When the answer quality level is favorable, the method continues at step 384 where the processing module issues a response that includes one or more answers based on the preliminary answers and/or further updated preliminary answers based on gathering further content. For example, the processing module generates a response that includes one or more answers and the answer quality level and issues the response to the requester.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 6A is a schematic block diagram of an embodiment of the element identification module 302 of FIG. 5A and the interpretation module 304 of FIG. 5A. The element identification module 302 includes an element matching module 400 and an element grouping module 402. The interpretation module 304 includes a grouping matching module 404 and a grouping interpretation module 406. Generally, an embodiment of this invention presents solutions where the element identification module 302 supports identifying potentially valid permutations of groupings of elements while the interpretation module 304 interprets the potentially valid permutations of groupings of elements to produce interpreted information that includes the most likely of groupings based on a question.

In an example of operation of the identifying of the potentially valid permutations of groupings of elements, when matching elements of the formatted content 314, the element matching module 400 generates matched elements 412 (e.g., identifiers of elements contained in the formatted content 314) based on the element list 332. For example, the element matching module 400 matches a received element to an element of the element list 332 and outputs the matched elements 412 to include an identifier of the matched element. When finding elements that are unidentified, the element matching module 400 outputs un-recognized words information 408 (e.g., words not in the element list 332, may temporarily add) as part of un-identified element information 342. For example, the element matching module 400 indicates that a match cannot be made between a received element of the formatted content 314, generates the unrecognized words info 408 to include the received element and/or a temporary identifier, and issues and updated element list 414 that includes the temporary identifier and the corresponding unidentified received element.

The element grouping module 402 analyzes the matched elements 412 in accordance with element rules 318 to produce grouping error information 410 (e.g., incorrect sentence structure indicators) when a structural error is detected. The element grouping module 402 produces identified element information 340 when favorable structure is associated with the matched elements in accordance with the element rules 318. The identified element information 340 may further include grouping information of the plurality of permutations of groups of elements (e.g., several possible interpretations), where the grouping information includes one or more groups of words forming an associated set and/or super-group set of two or more subsets when subsets share a common core element.

In an example of operation of the interpreting of the potentially valid permutations of groupings of elements to produce the interpreted information, the grouping matching module 404 analyzes the identified element information 340 in accordance with a groupings list 334 to produce validated groupings information 416. For example, the grouping matching module 404 compares a grouping aspect of the identified element information 340 (e.g., for each permutation of groups of elements of possible interpretations), generates the validated groupings information 416 to include identification of valid permutations aligned with the groupings list 334. Alternatively, or in addition to, the grouping matching module 404 generates an updated groupings list 418 when determining a new valid grouping (e.g., has favorable structure and interpreted meaning) that is to be added to the groupings list 334.

The grouping interpretation module 406 interprets the validated groupings information 416 based on the question information 346 and in accordance with the interpretation rules 320 to produce interpreted information 344 (e.g., most likely interpretations, next most likely interpretations, etc.). For example, the grouping interpretation module 406 obtains context, obtains favorable historical interpretations, processes the validated groupings based on interpretation rules 320, where each interpretation is associated with a correctness likelihood level.

FIG. 6B is a logic diagram of an embodiment of a method for interpreting information within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6A, and also FIG. 6B. The method includes step 430 where a processing module of one or more processing modules of one or more computing devices of the computing system analyzes formatted content. For example, the processing module attempt to match a received element of the formatted content to one or more elements of an elements list. When there is no match, the method branches to step 434 and when there is a match, the method continues to step 432. When there is a match, the method continues at step 432 for the processing module outputs matched elements (e.g., to include the matched element and/or an identifier of the matched element). When there is no match, the method continues at step 434 where the processing module outputs unrecognized words (e.g., elements and/or a temporary identifier for the unmatched element).

The method continues at step 436 for the processing module analyzes matched elements. For example, the processing module attempt to match a detected structure of the matched elements (e.g., chained elements as in a received sequence) to favorable structures in accordance with element rules. The method branches to step 440 when the analysis is unfavorable and the method continues to step 438 when the analysis is favorable. When the analysis is favorable matching a detected structure to the favorable structure of the element rules, the method continues at step 438 where the processing module outputs identified element information (e.g., an identifier of the favorable structure, identifiers of each of the detected elements). When the analysis is unfavorable matching a detected structure to the favorable structure of the element rules, the method continues at step 440 where the processing module outputs grouping error information (e.g., a representation of the incorrect structure, identifiers of the elements of the incorrect structure, a temporary new identifier of the incorrect structure).

The method continues at step 442 for the processing module analyzes the identified element information to produce validated groupings information. For example, the processing module compares a grouping aspect of the identified element information and generates the validated groupings information to include identification of valid permutations that align with the groupings list. Alternatively, or in addition to, the processing module generates an updated groupings list when determining a new valid grouping.

The method continues at step 444 where the processing module interprets the validated groupings information to produce interpreted information. For example, the processing module obtains one or more of context and historical interpretations and processes the validated groupings based on interpretation rules to generate the interpreted information, where each interpretation is associated with a correctness likelihood level (e.g., a quality level).

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 6C is a schematic block diagram of an embodiment of the answer resolution module 306 of FIG. 5A that includes an interim answer module 460, and answer prioritization module 462, and a preliminary answer quality module 464. Generally, an embodiment of this invention presents solutions where the answer resolution module 306 supports producing an answer for interpreted information 344.

In an example of operation of the providing of the answer, the interim answer module 460 analyzes the interpreted information 344 based on question information 346 and inferred question information 352 to produce interim answers 466 (e.g., answers to stated and/or inferred questions without regard to rules that is subject to further refinement). The answer prioritization module 462 analyzes the interim answers 466 based on answer rules 322 to produce preliminary answer 354. For example, the answer prioritization module 462 identifies all possible answers from the interim answers 466 that conform to the answer rules 322.

The preliminary answer quality module 464 analyzes the preliminary answers 354 in accordance with the question information 346, the inferred question information 352, and the answer rules 322 to produce an answer quality level 356. For example, for each of the preliminary answers 354, the preliminary answer quality module 464 may compare a fit of the preliminary answer 354 to a corresponding previous answer and question quality level, calculate the answer quality level 356 based on a level of conformance to the answer rules 322, calculate the answer quality level 356 based on alignment with the inferred question information 352, and determine the answer quality level 356 based on an interpreted correlation with the question information 346.

FIG. 6D is a logic diagram of an embodiment of a method for producing an answer within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-3, 4A-4C, 5E-5F, 6C, and also FIG. 6D. The method includes step 480 where a processing module of one or more processing modules of one or more computing devices of the computing system analyzes received interpreted information based on question information and inferred question information to produce one or more interim answers. For example, the processing module generates potential answers based on patterns consistent with previously produced knowledge and likelihood of correctness.

The method continues at step 482 where the processing module analyzes the one or more interim answers based on answer rules to produce preliminary answers. For example, the processing module identifies all possible answers from the interim answers that conform to the answer rules. The method continues at step 484 for the processing module analyzes the preliminary answers in accordance with the question information, the inferred question information, and the answer rules to produce an answer quality level. For example, for each of the elementary answers, the processing module may compare a fit of the preliminary answer to a corresponding previous answer-and-answer quality level, calculate the answer quality level based on performance to the answer rules, calculate answer quality level based on alignment with the inferred question information, and determine the answer quality level based on interpreted correlation with the question information.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 7A is an information flow diagram for interpreting information within a computing system, where sets of entigens 504 are interpreted from sets of identigens 502 which are interpreted from sentences of words 500. Such identigen entigen intelligence (IEI) processing of the words (e.g., to IEI process) includes producing one or more of interim knowledge, a preliminary answer, and an answer quality level. For example, the IEI processing includes identifying permutations of identigens of a phrase of a sentence (e.g., interpreting human expressions to produce identigen groupings for each word of ingested content), reducing the permutations of identigens (e.g., utilizing rules to eliminate unfavorable permutations), mapping the reduced permutations of identigens to at least one set of entigens (e.g., most likely identigens become the entigens) to produce the interim knowledge, processing the knowledge in accordance with a knowledge base (e.g., comparing the set of entigens to the knowledge base) to produce a preliminary answer, and generating the answer quality level based on the preliminary answer for a corresponding domain.

Human expressions are utilized to portray facts and fiction about the real world. The real-world includes items, actions, and attributes. The human expressions include textual words, textual symbols, images, and other sensorial information (e.g., sounds). It is known that many words, within a given language, can mean different things based on groupings and orderings of the words. For example, the sentences of words 500 can include many different forms of sentences that mean vastly different things even when the words are very similar.

The present invention presents solutions where the computing system 10 supports producing a computer-based representation of a truest meaning possible of the human expressions given the way that multitudes of human expressions relate to these meanings. As a first step of the flow diagram to transition from human representations of things to a most precise computer representation of the things, the computer identifies the words, phrases, sentences, etc. from the human expressions to produce the sets of identigens 502. Each identigen includes an identifier of their meaning and an identifier of an instance for each possible language, culture, etc. For example, the words car and automobile share a common meaning identifier but have different instance identifiers since they are different words and are spelled differently. As another example, the word duck is associated both with a bird and an action to elude even though they are spelled the same. In this example the bird duck has a different meaning than the elude duck and as such each has a different meaning identifier of the corresponding identigens.

As a second step of the flow diagram to transition from human representations of things to the most precise computer representation of the things, the computer extracts meaning from groupings of the identified words, phrases, sentences, etc. to produce the sets of entigens 504. Each entigen includes an identifier of a single conceivable and perceivable thing in space and time (e.g., independent of language and other aspects of the human expressions). For example, the words car and automobile are different instances of the same meaning and point to a common shared entigen. As another example, the word duck for the bird meaning has an associated unique entigen that is different than the entigen for the word duck for the elude meaning.

As a third step of the flow diagram to transition from human expressions of things to the most precise computer representation of the things, the computer reasons facts from the extracted meanings. For example, the computer maintains a fact-based of the valid meanings from the valid groupings or sets of entigens so as to support subsequent inferences, deductions, rationalizations of posed questions to produce answers that are aligned with a most factual view. As time goes on, and as an entigen has been identified, it can encounter an experience transformations in time, space, attributes, actions, and words which are used to identify it without creating contradictions or ever losing its identity.

FIG. 7B is a relationship block diagram illustrating an embodiment of relationships between things 510 and representations of things 512 within a computing system. The things 510 includes conceivable and perceivable things including actions 522, items 524, and attributes 526. The representation of things 512 includes representations of things used by humans 514 and representation of things used by of computing devices 516 of embodiments of the present invention. The things 510 relates to the representations of things used by humans 514 where the invention presents solutions where the computing system 10 supports mapping the representations of things used by humans 514 to the representations of things used by computing devices 516, where the representations of things used by computing devices 516 map back to the things 510.

The representations of things used by humans 514 includes textual words 528, textual symbols 530, images (e.g., non-textual) 532, and other sensorial information 534 (e.g., sounds, sensor data, electrical fields, voice inflections, emotion representations, facial expressions, whistles, etc.). The representations of things used by computing devices 516 includes identigens 518 and entigens 520. The representations of things used by humans 514 maps to the identigens 518 and the identigens 518 map to the entigens 520. The entigens 520 uniquely maps back to the things 510 in space and time, a truest meaning the computer is looking for to create knowledge and answer questions based on the knowledge.

To accommodate the mapping of the representations of things used by humans 514 to the identigens 518, the identigens 518 is partitioned into actenyms 544 (e.g., actions), itenyms 546 (e.g., items), attrenyms 548 (e.g., attributes), and functionals 550 (e.g., that join and/or describe). Each of the actenyms 544, itenyms 546, and attrenyms 548 may be further classified into singulatums 552 (e.g., identify one unique entigen) and pluratums 554 (e.g., identify a plurality of entigens that have similarities).

Each identigen 518 is associated with an identigens identifier (IDN) 536. The IDN 536 includes a meaning identifier (ID) 538 portion, an instance ID 540 portion, and a type ID 542 portion. The meaning ID 538 includes an identifier of common meaning. The instance ID 540 includes an identifier of a particular word and language. The type ID 542 includes one or more identifiers for actenyms, itenyms, attrenyms, singulatums, pluratums, a time reference, and any other reference to describe the IDN 536. The mapping of the representations of things used by humans 514 to the identigens 518 by the computing system of the present invention includes determining the identigens 518 in accordance with logic and instructions for forming groupings of words.

Generally, an embodiment of this invention presents solutions where the identigens 518 map to the entigens 520. Multiple identigens may map to a common unique entigen. The mapping of the identigens 518 to the entigens 520 by the computing system of the present invention includes determining entigens in accordance with logic and instructions for forming groupings of identigens.

FIG. 7C is a diagram of an embodiment of a synonym words table 570 within a computing system, where the synonym words table 570 includes multiple fields including textual words 572, identigens 518, and entigens 520. The identigens 518 includes fields for the meaning identifier (ID) 538 and the instance ID 540. The computing system of the present invention may utilize the synonym words table 570 to map textual words 572 to identigens 518 and map the identigens 518 to entigens 520. For example, the words car, automobile, auto, bil (Swedish), carro (Spanish), and bil (Danish) all share a common meaning but are different instances (e.g., different words and languages). The words map to a common meaning ID but to individual unique instant identifiers. Each of the different identigens map to a common entigen since they describe the same thing.

FIG. 7D is a diagram of an embodiment of a polysemous words table 576 within a computing system, where the polysemous words table 576 includes multiple fields including textual words 572, identigens 518, and entigens 520. The identigens 518 includes fields for the meaning identifier (ID) 538 and the instance ID 540. The computing system of the present invention may utilize the polysemous words table 576 to map textual words 572 to identigens 518 and map the identigens 518 to entigens 520. For example, the word duck maps to four different identigens since the word duck has four associated different meanings (e.g., bird, fabric, to submerge, to elude) and instances. Each of the identigens represent different things and hence map to four different entigens.

FIG. 7E is a diagram of an embodiment of transforming words into groupings within a computing system that includes a words table 580, a groupings of words section to validate permutations of groupings, and a groupings table 584 to capture the valid groupings. The words table 580 includes multiple fields including textual words 572, identigens 518, and entigens 520. The identigens 518 includes fields for the meaning identifier (ID) 538, the instance ID 540, and the type ID 542. The computing system of the present invention may utilize the words table 580 to map textual words 572 to identigens 518 and map the identigens 518 to entigens 520. For example, the word pilot may refer to a flyer and the action to fly. Each meaning has a different identigen and different entigen.

The computing system the present invention may apply rules to the fields of the words table 580 to validate various groupings of words. Those that are invalid are denoted with a “X” while those that are valid are associated with a check mark. For example, the grouping “pilot Tom” is invalid when the word pilot refers to flying and Tom refers to a person. The identigen combinations for the flying pilot and the person Tom are denoted as invalid by the rules. As another example, the grouping “pilot Tom” is valid when the word pilot refers to a flyer and Tom refers to the person. The identigen combinations for the flyer pilot and the person Tom are denoted as valid by the rules.

The groupings table 584 includes multiple fields including grouping ID 586, word strings 588, identigens 518, and entigens 520. The computing system of the present invention may produce the groupings table 584 as a stored fact base for valid and/or invalid groupings of words identified by their corresponding identigens. For example, the valid grouping “pilot Tom” referring to flyer Tom the person is represented with a grouping identifier of 3001 and identity and identifiers 150.001 and 457.001. The entigen field 520 may indicate associated entigens that correspond to the identigens. For example, entigen e717 corresponds to the flyer pilot meaning and entigen e61 corresponds to the time the person meaning. Alternatively, or in addition to, the entigen field 520 may be populated with a single entigen identifier (ENI).

The word strings field 588 may include any number of words in a string. Different ordering of the same words can produce multiple different strings and even different meanings and hence entigens. More broadly, each entry (e.g., role) of the groupings table 584 may refer to groupings of words, two or more word strings, an idiom, just identigens, just entigens, and/or any combination of the preceding elements. Each entry has a unique grouping identifier. An idiom may have a unique grouping ID and include identifiers of original word identigens and replacing identigens associated with the meaning of the idiom not just the meaning of the original words. Valid groupings may still have ambiguity on their own and may need more strings and/or context to select a best fit when interpreting a truest meaning of the grouping.

FIG. 8A is a data flow diagram for accumulating knowledge within a computing system, where a computing device, at a time=t0, ingests and processes facts 598 at a step 590 based on rules 316 and fact base information 600 to produce groupings 602 for storage in a fact base 592 (e.g., words, phrases, word groupings, identigens, entigens, quality levels). The facts 598 may include information from books, archive data, Central intelligence agency (CIA) world fact book, trusted content, etc. The ingesting may include filtering to organize and promote better valid groupings detection (e.g., considering similar domains together). The groupings 602 includes one or more of groupings identifiers, identigen identifiers, entigen identifiers, and estimated fit quality levels. The processing step 590 may include identifying identigens from words of the facts 598 in accordance with the rules 316 and the fact base info 600 and identifying groupings utilizing identigens in accordance with rules 316 and fact base info 600.

Subsequent to ingestion and processing of the facts 598 to establish the fact base 592, at a time=t1+, the computing device ingests and processes new content 604 at a step 594 in accordance with the rules 316 and the fact base information 600 to produce preliminary grouping 606. The new content may include updated content (e.g., timewise) from periodicals, newsfeeds, social media, etc. The preliminary grouping 606 includes one or more of preliminary groupings identifiers, preliminary identigen identifiers, preliminary entigen identifiers, estimated fit quality levels, and representations of unidentified words.

The computing device validates the preliminary groupings 606 at a step 596 based on the rules 316 and the fact base info 600 to produce updated fact base info 608 for storage in the fact base 592. The validating includes one or more of reasoning a fit of existing fact base info 600 with the new preliminary grouping 606, discarding preliminary groupings, updating just time frame information associated with an entry of the existing fact base info 600 (e.g., to validate knowledge for the present), creating new entigens, and creating a median entigen to summarize portions of knowledge within a median indicator as a quality level indicator (e.g., suggestive not certain).

Storage of the updated fact base information 608 captures patterns that develop by themselves instead of searching for patterns as in prior art artificial intelligence systems. Growth of the fact base 592 enables subsequent reasoning to create new knowledge including deduction, induction, inference, and inferential sentiment (e.g., a chain of sentiment sentences). Examples of sentiments includes emotion, beliefs, convictions, feelings, judgments, notions, opinions, and views.

FIG. 8B is a diagram of an embodiment of a groupings table 620 within a computing system. The groupings table 620 includes multiple fields including grouping ID 586, word strings 588, an IF string 622 and a THEN string 624. Each of the fields for the IF string 622 and the THEN string 624 includes fields for an identigen (IDN) string 626, and an entigen (ENI) string 628. The computing system of the present invention may produce the groupings table 620 as a stored fact base to enable IF THEN based inference to generate a new knowledge inference 630.

As a specific example, grouping 5493 points out the logic of IF someone has a tumor, THEN someone is sick and the grouping 5494 points of the logic that IF someone is sick, THEN someone is sad. As a result of utilizing inference, the new knowledge inference 630 may produce grouping 5495 where IF someone has a tumor, THEN someone is possibly sad (e.g., or is sad).

FIG. 8C is a data flow diagram for answering questions utilizing accumulated knowledge within a computing system, where a computing device ingests and processes question information 346 at a step 640 based on rules 316 and fact base info 600 from a fact base 592 to produce preliminary grouping 606. The ingesting and processing questions step 640 includes identifying identigens from words of a question in accordance with the rules 316 and the fact base information 600 and may also include identifying groupings from the identified identigens in accordance with the rules 316 and the fact base information 600.

The computing device validates the preliminary grouping 606 at a step 596 based on the rules 316 and the fact base information 600 to produce identified element information 340. For example, the computing device reasons fit of existing fact base information with new preliminary groupings 606 to produce the identified element information 340 associated with highest quality levels. The computing device interprets a question of the identified element information 340 at a step 642 based on the rules 316 and the fact base information 600. The interpreting of the question may include separating new content from the question and reducing the question based on the fact base information 600 and the new content.

The computing device produces preliminary answers 354 from the interpreted information 344 at a resolve answer step 644 based on the rules 316 and the fact base information 600. For example, the computing device compares the interpreted information 344 two the fact base information 600 to produce the preliminary answers 354 with highest quality levels utilizing one or more of deduction, induction, inferencing, and applying inferential sentiments logic. Alternatively, or in addition to, the computing device may save new knowledge identified from the question information 346 to update the fact base 592.

FIG. 8D is a data flow diagram for answering questions utilizing interference within a computing system that includes a groupings table 648 and the resolve answer step 644 of FIG. 8C. The groupings table 648 includes multiple fields including fields for a grouping (GRP) identifier (ID) 586, word strings 588, an identigen (IDN) string 626, and an entigen (ENI) 628. The groupings table 648 may be utilized to build a fact base to enable resolving a future question into an answer. For example, the grouping 8356 notes knowledge that Michael sleeps eight hours and grouping 8357 notes that Michael usually starts to sleep at 11 PM.

In a first question example that includes a question “Michael sleeping?”, the resolve answer step 644 analyzes the question from the interpreted information 344 in accordance with the fact base information 600, the rules 316, and a real-time indicator that the current time is 1 AM to produce a preliminary answer of “possibly YES” when inferring that Michael is probably sleeping at 1 AM when Michael usually starts sleeping at 11 PM and Michael usually sleeps for a duration of eight hours.

In a second question example that includes the question “Michael sleeping?”, the resolve answer step 644 analyzes the question from the interpreted information 344 in accordance with the fact base information 600, the rules 316, and a real-time indicator that the current time is now 11 AM to produce a preliminary answer of “possibly NO” when inferring that Michael is probably not sleeping at 11 AM when Michael usually starts sleeping at 11 PM and Michael usually sleeps for a duration of eight hours.

FIG. 8E is a relationship block diagram illustrating another embodiment of relationships between things and representations of things within a computing system. While things in the real world are described with words, it is often the case that a particular word has multiple meanings in isolation. Interpreting the meaning of the particular word may hinge on analyzing how the word is utilized in a phrase, a sentence, multiple sentences, paragraphs, and even whole documents or more. Describing and stratifying the use of words, word types, and possible meanings help in interpreting a true meaning.

Humans utilize textual words 528 to represent things in the real world. Quite often a particular word has multiple instances of different grammatical use when part of a phrase of one or more sentences. The grammatical use 649 of words includes the nouns and the verbs, and also includes adverbs, adjectives, pronouns, conjunctions, prepositions, determiners, exclamations, etc.

As an example of multiple grammatical use, the word “bat” in the English language can be utilized as a noun or a verb. For instance, when utilized as a noun, the word “bat” may apply to a baseball bat or may apply to a flying “bat.” As another instance, when utilized as a verb, the word “bat” may apply to the action of hitting or batting an object, i.e., “bat the ball.”

To stratify word types by use, the words are associated with a word type (e.g., type identifier 542). The word types include objects (e.g., items 524), characteristics (e.g., attributes 526), actions 522, and the functionals 550 for joining other words and describing words. For example, when the word “bat” is utilized as a noun, the word is describing the object of either the baseball bat or the flying bat. As another example, when the word “bat” is utilized as a verb, the word is describing the action of hitting.

To determine possible meanings, the words, by word type, are mapped to associative meanings (e.g., identigens 718). For each possible associative meaning, the word type is documented with the meaning and further with an identifier (ID) of the instance (e.g., an identigen identifier).

For the example of the word “bat” when utilized as a noun for the baseball bat, a first identigen identifier 536-1 includes a type ID 542-1 associated with the object 524, an instance ID 540-1 associated with the first identigen identifier (e.g., unique for the baseball bat), and a meaning ID 538-1 associated with the baseball bat. For the example of the word “bat” when utilized as a noun for the flying bat, a second identigen identifier 536-2 includes a type ID 542-1 associated with the object 524, an instance ID 540-2 associated with the second identigen identifier (e.g., unique for the flying bat), and a meaning ID 538-2 associated with the flying bat. For the example of the word “bat” when utilized as a verb for the bat that hits, a third identigen identifier 536-2 includes a type ID 542-2 associated with the actions 522, an instance ID 540-3 associated with the third identigen identifier (e.g., unique for the bat that hits), and a meaning ID 538-3 associated with the bat that hits.

With the word described by a type and possible associative meanings, a combination of full grammatical use of the word within the phrase etc., application of rules, and utilization of an ever-growing knowledge base that represents knowledge by linked entigens, the absolute meaning (e.g., entigen 520) of the word is represented as a unique entigen. For example, a first entigen e1 represents the absolute meaning of a baseball bat (e.g., a generic baseball bat not a particular baseball bat that belongs to anyone), a second entigen e2 represents the absolute meaning of the flying bat (e.g., a generic flying bat not a particular flying bat), and a third entigen e3 represents the absolute meaning of the verb bat (e.g., to hit).

An embodiment of methods to ingest text to produce absolute meanings for storage in a knowledge base are discussed in greater detail with reference to FIGS. 8F-H. Those embodiments further discuss the discerning of the grammatical use, the use of the rules, and the utilization of the knowledge base to definitively interpret the absolute meaning of a string of words.

Another embodiment of methods to respond to a query to produce an answer based on knowledge stored in the knowledge base are discussed in greater detail with reference to FIGS. 8J-L. Those embodiments further discuss the discerning of the grammatical use, the use of the rules, and the utilization of the knowledge base to interpret the query. The query interpretation is utilized to extract the answer from the knowledge base to facilitate forming the query response.

FIGS. 8F and 8G are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module 300 of FIG. 5E, the element identification module 302 of FIG. 5E, the interpretation module 304 of FIG. 5E, the IEI control module 308 of FIG. 5E, and the SS memory 96 of FIG. 2. Generally, an embodiment of this invention provides presents solutions where the computing system 10 supports processing content to produce knowledge for storage in a knowledge base.

The processing of the content to produce the knowledge includes a series of steps. For example, a first step includes identifying words of an ingested phrase to produce tokenized words. As depicted in FIG. 8F, a specific example of the first step includes the content ingestion module 300 comparing words of source content 310 to dictionary entries to produce formatted content 314 that includes identifiers of known words. Alternatively, when a comparison is unfavorable, the temporary identifier may be assigned to an unknown word. For instance, the content ingestion module 300 produces identifiers associated with the words “the”, “black”, “bat”, “eats”, and “fruit” when the ingested phrase includes “The black bat eats fruit”, and generates the formatted content 314 to include the identifiers of the words.

A second step of the processing of the content to produce the knowledge includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in FIG. 8F, a specific example of the second step includes the element identification module 302 performing a look up of identigen identifiers, utilizing an element list 332 and in accordance with element rules 318, of the one or more identigens associated with each tokenized word of the formatted content 314 to produce identified element information 340.

A unique identifier is associated with each of the potential object, the characteristic, and the action (OCA) associated with the tokenized word (e.g. sequential identigens). For instance, the element identification module 302 identifies a functional symbol for “the”, identifies a single identigen for “black”, identifies two identigens for “bat” (e.g., baseball bat and flying bat), identifies a single identigen for “eats”, and identifies a single identigen for “fruit.” When at least one tokenized word is associated with multiple identigens, two or more permutations of sequential combinations of identigens for each tokenized word result. For example, when “bat” is associated with two identigens, two permutations of sequential combinations of identigens result for the ingested phrase.

A third step of the processing of the content to produce the knowledge includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens (e.g., relationships), where each sequential linking pairs a preceding identigen to a next identigen, and where an equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. Multiple OCAs occur for a common word when the word has multiple potential meanings (e.g., a baseball bat, a flying bat).

As depicted in FIG. 8F, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word of the identified element information 340, the interpretation module 304 generates, in accordance with interpretation rules 320 and a groupings list 334, an equation package to include one or more of the identifiers of the tokenized words, a list of identifiers of the identigens of the equation package, a list of pairing identifiers for sequential pairs of identigens, and a quality metric associated with each sequential pair of identigens (e.g., likelihood of a proper interpretation). For instance, the interpretation module 304 produces a first equation package that includes a first identigen pairing of a black bat (e.g., flying bat with a higher quality metric level), the second pairing of bat eats (e.g., the flying bat eats, with a higher quality metric level), and a third pairing of eats fruit, and the interpretation module 304 produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit.

A fourth step of the processing of the content to produce the knowledge includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in FIG. 8F, a specific example of the fourth step includes the interpretation module 304 applying interpretation rules 320 (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce a number of permutations of the sequential combinations of identigens to produce interpreted information 344 that includes identification of at least one equation package as a surviving interpretation SI (e.g., higher quality metric level).

Non-surviving equation packages are eliminated that compare unfavorably to pairing rules and/or are associated with an unfavorable quality metric levels to produce a non-surviving interpretation NSI 2 (e.g., lower quality metric level), where an overall quality metric level may be assigned to each equation package based on quality metric levels of each pairing, such that a higher quality metric level of an equation package indicates a higher probability of a most favorable interpretation. For instance, the interpretation module 304 eliminates the equation package that includes the second pairing indicating that the “baseball bat eats” which is inconsistent with a desired quality metric level of one or more of the groupings list 334 and the interpretation rules 320 and selects the equation package associated with the “flying bat eats” which is favorably consistent with the one or more of the quality metric levels of the groupings list 334 and the interpretation rules 320.

A fifth step of the processing of the content to produce the knowledge utilizing the confidence level includes integrating knowledge of the surviving equation package into a knowledge base. For example, integrating at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. As another example, the portion of the reduced OCA combinations may be translated into rows and columns entries when utilizing a rows and columns database rather than a graphical database. When utilizing the rows and columns approach for the knowledge base, subsequent access to the knowledge base may utilize structured query language (SQL) queries.

As depicted in FIG. 8G, a specific example of the fifth step includes the IEI control module 308 recovering fact base information 600 from SS memory 96 to identify a portion of the knowledge base for potential modification utilizing the OCAs of the surviving interpretation SI 1 (i.e., compare a pattern of relationships between the OCAs of the surviving interpretation SI 1 from the interpreted information 344 to relationships of OCAs of the portion of the knowledge base including potentially new quality metric levels).

The fifth step further includes determining modifications (e.g., additions, subtractions, further clarifications required when information is complex, etc.) to the portion of the knowledge base based on the new quality metric levels. For instance, the IEI control module 308 causes adding the element “black” as a “describes” relationship of an existing bat OCA and adding the element “fruit” as an eats “does to” relationship to implement the modifications to the portion of the fact base information 600 to produce updated fact base information 608 for storage in the SS memory 96.

FIG. 8H is a logic diagram of an embodiment of a method for processing content to produce knowledge for storage within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8E, 8F, and also FIG. 8G. The method includes step 650 where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested phrase to produce tokenized words. The identified includes comparing words to known words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 651 where the processing module identifies one or more identigens that corresponds to the tokenized word, where each identigen describes one of an object, a characteristic, and an action (e.g., OCA). The identifying includes performing a lookup of identifiers of the one or more identigens associated with each tokenized word, where he different identifiers associated with each of the potential object, the characteristic, and the action associated with the tokenized word.

The method continues at step 652 where the processing module, for each permutation of sequential combinations of identigens, generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module generates the equation package to include a plurality of equation elements, where each equation element describes the relationship (e.g., describes, acts on, is a, belongs to, did, did too, etc.) between sequentially adjacent identigens of a plurality of sequential combinations of identigens. Each equation element may be further associated with a quality metric to evaluate a favorability level of an interpretation in light of the sequence of identigens of the equation package.

The method continues at step 653 where the processing module selects a surviving equation package associated with most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens), to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package, where non-surviving equation packages are eliminated the compare unfavorably to pairing rules and/or are associated with an unfavorable quality metric levels to produce a non-surviving interpretation, where an overall quality metric level is assigned to each equation package based on quality metric levels of each pairing, such that a higher quality metric level indicates an equation package with a higher probability of favorability of correctness.

The method continues at step 654 where the processing module integrates knowledge of the surviving equation package into a knowledge base. For example, the processing module integrates at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. The integrating may include recovering fact base information from storage of the knowledge base to identify a portion of the knowledge base for potential modifications utilizing the OCAs of the surviving equation package (i.e., compare a pattern of relationships between the OCAs of the surviving equation package to relationships of the OCAs of the portion of the knowledge base including potentially new quality metric levels). The integrating further includes determining modifications (e.g., additions, subtractions, further clarifications required when complex information is presented, etc.) to produce the updated knowledge base that is based on fit of acceptable quality metric levels, and implementing the modifications to the portion of the fact base information to produce the updated fact base information for storage in the portion of the knowledge base.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIGS. 8J and 8K are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module 300 of FIG. 5E, the element identification module 302 of FIG. 5E, the interpretation module 304 of FIG. 5E, the answer resolution module 306 of FIG. 5E, and the SS memory 96 of FIG. 2. Generally, an embodiment of this invention provides solutions where the computing system 10 supports for generating a query response to a query utilizing a knowledge base.

The generating of the query response to the query includes a series of steps. For example, a first step includes identifying words of an ingested query to produce tokenized words. As depicted in FIG. 8J, a specific example of the first step includes the content ingestion module 300 comparing words of query info 138 to dictionary entries to produce formatted content 314 that includes identifiers of known words. For instance, the content ingestion module 300 produces identifiers for each word of the query “what black animal flies and eats fruit and insects?”

A second step of the generating of the query response to the query includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action (OCA). As depicted in FIG. 8J, a specific example of the second step includes the element identification module 302 performing a look up of identifiers, utilizing an element list 332 and in accordance with element rules 318, of the one or more identigens associated with each tokenized word of the formatted content 314 to produce identified element information 340. A unique identifier is associated with each of the potential object, the characteristic, and the action associated with a particular tokenized word. For instance, the element identification module 302 produces a single identigen identifier for each of the black color, an animal, flies, eats, fruit, and insects.

A third step of the generating of the query response to the query includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation). The equation package includes a sequential linking of pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc.

As depicted in FIG. 8J, a specific example of the third step includes the interpretation module 304, for each permutation of identigens of each tokenized word of the identified element information 340, generating the equation packages in accordance with interpretation rules 320 and a groupings list 334 to produce a series of equation elements that include pairings of identigens. For instance, the interpretation module 304 generates a first pairing to describe a black animal, a second pairing to describe an animal that flies, a third pairing to describe flies and eats, a fourth pairing to describe eats fruit, and a fifth pairing to describe eats fruit and insects.

A fourth step of the generating the query response to the query includes selecting a surviving equation package associated with a most favorable interpretation. As depicted in FIG. 8J, a specific example of the fourth step includes the interpretation module 304 applying the interpretation rules 320 (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to produce interpreted information 344. The interpreted information 344 includes identification of at least one equation package as a surviving interpretation SI 10, where non-surviving equation packages, if any, are eliminated that compare unfavorably to pairing rules to produce a non-surviving interpretation.

A fifth step of the generating the query response to the query includes utilizing a knowledge base, generating a query response to the surviving equation package of the query, where the surviving equation package of the query is transformed to produce query knowledge for comparison to a portion of the knowledge base. An answer is extracted from the portion of the knowledge base to produce the query response.

As depicted in FIG. 8K, a specific example of the fifth step includes the answer resolution module 306 interpreting the surviving interpretation SI 10 of the interpreted information 344 in accordance with answer rules 322 to produce query knowledge QK 10 (i.e., a graphical representation of knowledge when the knowledge base utilizes a graphical database). For example, the answer resolution module 306 accesses fact base information 600 from the SS memory 96 to identify the portion of the knowledge base associated with a favorable comparison of the query knowledge QK 10 (e.g., by comparing attributes of the query knowledge QK 10 to attributes of the fact base information 600), and generates preliminary answers 354 that includes the answer to the query. For instance, the answer is “bat” when the associated OCAs of bat, such as black, eats fruit, eats insects, is an animal, and flies, aligns with OCAs of the query knowledge.

FIG. 8L is a logic diagram of an embodiment of a method for generating a query response to a query utilizing knowledge within a knowledge base within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, 8J, and also FIG. 8K. The method includes step 655 where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested query to produce tokenized words. For example, the processing module compares words to known words of dictionary entries to produce identifiers of known words.

For each tokenized word, the method continues at step 656 where the processing module identifies one or more identigens that correspond to the tokenized word, where each identigen describes one of an object, a characteristic, and an action. For example, the processing module performs a lookup of identifiers of the one or more identigens associated with each tokenized word, where different identifiers associated with each permutation of a potential object, characteristic, and action associated with the tokenized word.

For each permutation of sequential combinations of identigens, the method continues at step 657 where the processing module generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module includes all other permutations of all other tokenized words to generate the equation packages. Each equation package includes a plurality of equation elements describing the relationships between sequentially adjacent identigens of a plurality of sequential combinations of identigens.

The method continues at step 658 where the processing module selects a surviving equation package associated with a most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package. Non-surviving equation packages are eliminated the compare unfavorably to pairing rules.

The method continues at step 659 where the processing module generates a query response to the surviving equation package, where the surviving equation package is transformed to produce query knowledge for locating the portion of a knowledge base that includes an answer to the query. As an example of generating the query response, the processing module interprets the surviving the equation package in accordance with answer rules to produce the query knowledge (e.g., a graphical representation of knowledge when the knowledge base utilizes a graphical database format).

The processing module accesses fact base information from the knowledge base to identify the portion of the knowledge base associated with a favorable comparison of the query knowledge (e.g., favorable comparison of attributes of the query knowledge to the portion of the knowledge base, aligning favorably comparing entigens without conflicting entigens). The processing module extracts an answer from the portion of the knowledge base to produce the query response.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 9A is a schematic block diagram of another embodiment of a computing system that includes media sources 660, the artificial intelligence (AI) server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The media sources 660 includes the content sources 16-1 through 16-N of FIG. 1. In particular, content sources associated with media including one or more of newsfeeds, social media, press releases, blogs, periodicals, etc. The AI server 20-1 includes the processing module 50-1 of FIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processing module 50-1 includes the collections module 120 of FIG. 4A, the identigen entigen intelligence (IEI) module 122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally, an embodiment of this invention provides a solution to produce a response to a query.

In an example of operation of the responding to the query, the query module 124 interprets a received query request 136 to produce query requirements. The interpreting includes one or more of determining content requirements, determining source requirements, determining answer timing requirements, and identifying at least one domain associated with the query request 136. For example, the query module 124 determines the content requirements to include facts around the Senate tax bill, determines the source requirements to include the media sources 660, determines the answer timing requirements to include a two hour time frame, and identifies politics as the domain when receiving the query request 136 that includes a question “for the next two hours, which media sources are most associated with fake news pertaining to the Senate tax bill?”\

Having produced the query requirements, the query module 124 issues at least one of an IEI request 244 and a collections request 132 based on the query request 136. For example, the query module 124 generates the IEI request 244 and sends the IEI request 244 to the IEI module 122 when the source requirements suggest that the IEI module 122 is able to provide an immediate response. As another example, the query module 124 generates the collections request 132 and sends the collections request 132 to the collections module 120 when the source requirements suggest that a future time frame is associated with the query request 136 and more content is required. For instance, the query module 124 issues the collections request 132 to the collections module 122 facilitate collecting content over the next two hours and subsequently issues the IEI request 244 to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEI request 244 to produce human expressions that include question content and question information. The formatting includes analyzing the IEI request 244 for recognizable human expressions of question content and question information in accordance with rules and fact base information 600 (e.g., facts pertaining to the Senate tax bill) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEI processing” to the human expressions to produce one or more of new knowledge, a preliminary answer, and an answer quality level associated with the preliminary answer. The IEI processing includes identifying permutations of identigens, reducing the permutations in accordance with the rules, mapping the reduced permutations of identigens to entigens to generate knowledge, processing the knowledge in accordance with the fact base (e.g., fact base info 600) to produce the preliminary answer, and generating the answer quality level based on the preliminary answer and the request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issues a collections request 132 to the collections module 120 to gather more content to produce knowledge to enable a desired favorable quality level of the answer. The issuing includes generating the collections request 132 based on one or more of the IEI requests 244, the preliminary answer, elements of the fact base information 600 (e.g., the present knowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests 132 to produce content requirements. The interpreting includes one or more of determining content selection requirements, determining source selection requirements, and determining content acquisition timing requirements. For example, the collections module 120 determines the source selection requirements to include selecting the content sources 16-1 through 16-N of the media sources 660, determines the content selection requirements to include content associated with the Senate tax bill, and determines the content acquisition timing requirements to include a two hour time span.

Having produced the content requirements, the collections module 120 issues a plurality of content requests 126 to a plurality of content sources identified by the content requirements (e.g., to the content sources 16-1 through 16-N). For example, the collections module 120 identifies the plurality of content sources, generates the content requests based on the content requirements, and sends the plurality of content requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collections module 120 interprets a plurality of content responses 128 to determine whether a response quality level is favorable. The interpreting includes analyzing the plurality of content responses 128 to produce an estimated response quality level, and indicating a favorable response quality level when the estimated response quality level compares favorably to a minimum response quality threshold level (e.g., greater than). When the response quality level is favorable, the collections module 120 issues a collections response 134 to the IEI module 122, where the collections response 134 includes further content. For example, the collections module 120 generates the collections response 134 to include the further content and the estimated response quality level, and sends the collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more of the IEI request 244 and the fact base information 600 to produce one or more of updated fact base information (e.g., new knowledge for storage in the SS memory 96) and a preliminary answer with an associated preliminary answer quality level. For example, the IEI module 122 reasons the further content with the fact base information 600 to produce the preliminary answer which identifies which sources are trusted and those that are not trusted (e.g., fake news). When the answer quality level is favorable, the IEI module 122 issues an IEI response 246 to the query module 124 where the IEI response 246 includes the preliminary answer associated with a favorable answer quality level. The query module 124 interprets the received answer to produce a quality level of the received answer. For example, the query module 124 analyzes the preliminary answer in accordance with the query requirements and the rules to generate the quality level of the received answer. When the quality level of the received answer is favorable, the query module 124 issues a query response 140 to the user device 12-1, where the query response 140 includes the answer associated with the favorable quality level of the answer.

FIG. 9B is a data flow diagram for answering questions utilizing accumulated knowledge within a computing system. The data flow diagram includes the IEI module 122 of FIG. 9A and fact base information 600 in the form of trusted sources 670 and un-trusted sources 672. The trusted sources 670 includes a plurality of source T1-TN groupings table 674 and the trusted sources 672 includes a plurality of source U1-UN groupings table 676. Each groupings table 674 and 676 includes multiple fields including fields for a group (GRP) identifier (ID) 586, word strings 588, identigen (IDN) string 626, and an entigen (ENI) 628. For instance, the groupings tables 674 of the trusted sources 670 includes word strings and identifiers associated with trusted facts, such as the Senate tax bill has five tax brackets and Senator Smith supports the Senate tax bill. As another instance, the groupings tables 676 of the un-trusted sources 672 includes conflicting data such as 4 to 6 tax brackets and Senator Smith does not support or may not support the Senate tax bill.

As an example of operation of providing an answer to a query, the IEI module 122 interprets the IEI request 244, facilitates obtaining the fact base information 600, and generates the preliminary answer based on the rules 316 and associated time frames relevant to the question of the IEI request 244. For example, the IEI module 122 generates the preliminary answer to indicate that “fake is most likely from sources U1-UN”, when the sources U1-UN are affiliated with the un-trusted sources 672. For instance, the IEI module 122 affiliates the sources U1-UN with the un-trusted sources 672 when one or more triggers occurs including source content does not align (e.g., even one instance of discrepancy) with the fact base info 600 of the trusted sources 670, inaccuracies from each source is above a maximum inaccuracy threshold level (e.g., too many inaccuracies), when content elements align with a minority of other sources (e.g., sources have same misinformation), and when content quality level falls below a desired content quality threshold level.

FIG. 9C is a logic diagram of an embodiment of a method for producing a response to a query within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, 9A-9B, and also FIG. 9C. The method includes step 680 where a processing module of one or more processing modules of one or more computing devices of the computing system interprets a received query request from a requester to produce query requirements. The interpreting includes one or more of determining content requirements, (e.g., to identify and trusted sources), determining source requirements, determining answer timing requirements, and identifying a domain associated with the query request.

The method continues at step 682 where the processing module IEI processes human expressions of the received query request based on a fact base generated from previous content to produce a preliminary answer. The processing may include formatting portions of the query request in accordance with formatting rules to produce recognizable human expressions of content and question information. For example, the processing module produces the question information to include a request to identify un-trusted sources for a particular domain (e.g., identifying fake news outlets). The processing may further include identifying permutations of identigens within the human expressions, reducing the permutations, mapping the reduce permutations to entigens to produce knowledge, processing the knowledge in accordance with a fact base to produce the preliminary answer, and generating an answer quality level associated with the preliminary answer. For instance, the processing module generates a relatively low answer quality level when the question relates to gathering information over a subsequent two hours such that more content must be gathered to produce an answer associated with a higher and more favorable answer quality level.

When the answer quality level is unfavorable, the method continues at step 684 where the processing module generates content requirements. The generating of the content requirements includes determining, based on one or more of the query requirements, preliminary answer, and the answer quality level, one or more of content selection requirements, source selection requirements, and acquisition timing requirements.

The method continues at step 686 for the processing module obtains further content from a plurality of sources based on the content requirements. For example, the processing module identifies the plurality of content sources, generates content requests based on the content requirements, and sends the plurality of content requests to the plurality of identified content sources, analyzes a plurality of content responses to produce an estimated quality level, indicates favorable quality level when the estimated quality level compares favorably to a minimum quality threshold level, and indicates unfavorable quality level to facilitate collective more content when the estimated quality level compares unfavorably to the minimum quality threshold level.

The method continues at step 688 where the processing module IEI processes human expressions of the further content based on the fact base to produce an updated preliminary answer that identifies at least one of trusted and un-trusted sources. For example, the processing module analyzes, based on one or more of the query request, the fact base info associated with the identified domain, and the further content to produce one or more of updated fact base info (e.g., new knowledge), the updated preliminary answer (e.g., updated list of trusted and on trusted sources), and an associated answer quality level. The analyzing may include reasoning the further content with the fact base to produce the updated fact base info and the preliminary answer to include which sources are trusted and which sources are un-trusted.

When the updated answer quality level is favorable, the method continues at step 690 where the processing module issues a query response to the request are that identifies at least one of the un-trusted and trusted sources. The issuing includes one or more of analyzing the preliminary answers in accordance with the query requirements and the rules to generate the updated quality level, generating the query response to include the answer associated with favorable quality level, and sending the query response to the requester

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 10A is a schematic block diagram of another embodiment of a computing system that includes event content sources 700, the artificial intelligence (AI) server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The event content sources 700 includes the content sources 16-1 through 16-N of FIG. 1. In particular, content sources associated with events including one or more of physical world data (environmental, structural, machine, etc.), community beliefs (e.g., social media), and news outlet sources (e.g., press releases, periodicals, radio broadcast, television news, financial market news, etc.), etc. The AI server 20-1 includes the processing module 50-1 of FIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processing module 50-1 includes the collections module 120 of FIG. 4A, the identigen entigen intelligence (IEI) module 122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally, an embodiment of this invention provides a solution to produce a response to a query regarding predicting an occurrence (e.g., of a physical event, storm damage, broken machine damage, a cyber attack, a physical attack, a political attack, of a thought transition, etc.).

In an example of operation of the responding to the query, the query module 124 interprets a received query request 136 to produce query requirements. The interpreting includes one or more of determining content requirements, determining source requirements, determining answer timing requirements, and identifying at least one domain associated with the query request 136. For example, the query module 124 determines the content requirements to include facts that can lead to prediction of the occurrence, determines the source requirements to include the event content sources 700, determines the answer timing requirements to include a timeframe associated with the predicted occurrence, and identifies a particular type of occurrence as the domain when receiving the query request 136 that includes a question “provide a temporal view of likelihood of an [event, condition, state . . . ] occurring.”

Having produced the query requirements, the query module 124 issues at least one of an IEI request 244 and a collections request 132 based on the query request 136. For example, the query module 124 generates the IEI request 244 and sends the IEI request 244 to the IEI module 122 when the source requirements suggest that the IEI module 122 is able to provide an immediate response. As another example, the query module 124 generates the collections request 132 and sends the collections request 132 to the collections module 120 when the source requirements suggest that a future time frame is associated with the query request 136 and more content is required. For instance, the query module 124 issues the collections request 132 to the collections module 122 facilitate collecting content over the next 20 minutes associated with a typical occurrence of the query request 136 and subsequently issues the IEI request 244 to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEI request 244 to produce human expressions that include question content and question information. The formatting includes analyzing the IEI request 244 for recognizable human expressions of question content and question information in accordance with rules and fact base information 600 (e.g., facts pertaining to the occurrence) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEI processing” to the human expressions to produce one or more of new knowledge, a preliminary answer, and an answer quality level associated with the preliminary answer. The IEI processing includes identifying permutations of identigens, reducing the permutations in accordance with the rules, mapping the reduced permutations of identigens to entigens to generate knowledge, processing the knowledge in accordance with the fact base (e.g., fact base info 600) to produce the preliminary answer, and generating the answer quality level based on the preliminary answer and the request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issues a collections request 132 to the collections module 120 to gather more content to produce knowledge to enable a desired favorable quality level of the answer. The issuing includes generating the collections request 132 based on one or more of the IEI requests 244, the preliminary answer, elements of the fact base information 600 (e.g., the present knowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests 132 to produce content requirements. The interpreting includes one or more of determining content selection requirements, determining source selection requirements, and determining content acquisition timing requirements. For example, the collections module 120 determines the source selection requirements to include selecting the content sources 16-1 through 16-N of the event content sources 700, determines the content selection requirements to include content associated with the occurrence (e.g., scenarios that are affiliated with the occurrence), and determines the content acquisition timing requirements to include a time span for collection if any.

Having produced the content requirements, the collections module 120 issues a plurality of content requests 126 to a plurality of content sources identified by the content requirements (e.g., to the content sources 16-1 through 16-N). For example, the collections module 120 identifies the plurality of content sources, generates the content requests based on the content requirements, and sends the plurality of content requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collections module 120 interprets a plurality of content responses 128 to determine whether a response quality level is favorable. The interpreting includes analyzing the plurality of content responses 128 to produce an estimated response quality level, and indicating a favorable response quality level when the estimated response quality level compares favorably to a minimum response quality threshold level (e.g., greater than). When the response quality level is favorable, the collections module 120 issues a collections response 134 to the IEI module 122, where the collections response 134 includes further content. For example, the collections module 120 generates the collections response 134 to include the further content and the estimated response quality level, and sends the collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more of the IEI request 244 and the fact base information 600 to produce one or more of updated fact base information (e.g., new knowledge for storage in the SS memory 96) and a preliminary answer with an associated preliminary answer quality level. For example, the IEI module 122 reasons the further content with the fact base information 600 to produce the preliminary answer which predicts the likelihood of the occurrence. When the answer quality level is favorable, the IEI module 122 issues an IEI response 246 to the query module 124 where the IEI response 246 includes the preliminary answer associated with a favorable answer quality level. The query module 124 interprets the received answer to produce a quality level of the received answer. For example, the query module 124 analyzes the preliminary answer in accordance with the query requirements and the rules to generate the quality level of the received answer. When the quality level of the received answer is favorable, the query module 124 issues a query response 140 to the user device 12-1, where the query response 140 includes the answer associated with the favorable quality level of the answer.

FIG. 10B is a data flow diagram for predicting an occurrence utilizing pre-occurrence sequence detection within a computing system, where a computing device of the computing system performs the resolve answer step 644, based on rules 316, time 702, and fact base info 600, on content that includes an estimated value and desired range for each of n conditions for each N sequences to produce preliminary answers 354. Each condition of the content describes status of an outside force that can be determined based on fact base info 600 (e.g., physical, beliefs, statements, etc.). The computing device compares the estimated value of the condition to a desired range (e.g., minimum/maximum of a metric) associated with the condition to produce the status (e.g., probability of a factual element based on the comparison. Each sequence includes an ordered series of conditions that are estimated to have values that compare favorably to an associated desired value range to complete the sequence (e.g., ordering may be strict or flexible). The plurality of sequences may include any number of sequences to link to the occurrence.

In an example of operation, one sequence is utilized with two conditions to provide an estimated factory machine failure occurrence, where the first condition is a text stream from an input power monitor indicating that proper power (e.g., within a desired range associated with the input power, and the second condition is a data stream from a revolutions per minute (RPM) monitor of the machine output. The computing device obtains the content for the first and second conditions, and generates a preliminary answer 354 that indicates that the factory machine is expected to fail, when the input power is within the desired range and that the machine output is associated with a lower than expected rpm value, and the fact base info 600 includes a grouping that states that the factory machine fails when the input power is proper but the rpm output is too low.

FIG. 10C is a logic diagram of an embodiment of a method for predicting an occurrence within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, 10A-10B, and also FIG. 10C. The method includes step 720 where a processing module of one or more processing modules of one or more computing devices of the computing system interprets a received query request from a requester to produce query requirements with regards to an occurrence. The interpreting includes one or more of determining content requirements, (e.g., to gather conditions of sequences), determining source requirements, determining answer timing requirements, and identifying a domain associated with the query request.

The method continues at step 722 where the processing module IEI processes human expressions of the received query request based on a fact base generated from previous content to produce a preliminary occurrence answer. The processing may include formatting portions of the query request in accordance with formatting rules to produce recognizable human expressions of content and question information. For example, the processing module produces the question information to include a request to determine likelihood of an occurrence (e.g., identifying conditions and scenarios that lead to the occurrence). The processing may further include identifying permutations of identigens within the human expressions, reducing the permutations, mapping the reduce permutations to entigens to produce knowledge, processing the knowledge in accordance with a fact base to produce the preliminary answer, and generating an answer quality level associated with the preliminary answer. For instance, the processing module generates a relatively low answer quality level when the question relates to gathering information over a subsequent time frame such that more content must be gathered to produce an answer associated with a higher and more favorable answer quality level (e.g., start looking for values of conditions associated with scenarios to support answering the likelihood of occurrence question).

When the answer quality level is unfavorable, the method continues at step 724 where the processing module generates content requirements. The generating of the content requirements includes determining, based on one or more of the query requirements, preliminary answer, and the answer quality level, one or more of content selection requirements, source selection requirements, and acquisition timing requirements.

The method continues at step 726 where the processing module obtains further content from a plurality of sources based on the content requirements. For example, the processing module identifies the plurality of content sources, generates content requests based on the content requirements, and sends the plurality of content requests to the plurality of identified content sources, analyzes a plurality of content responses to produce an estimated quality level, indicates favorable quality level when the estimated quality level compares favorably to a minimum quality threshold level, and indicates unfavorable quality level to facilitate collective more content when the estimated quality level compares unfavorably to the minimum quality threshold level.

The method continues at step 728 where the processing module IEI processes human expressions of the further content based on the fact base to produce an updated preliminary occurrence answer that identifies the likelihood of the occurrence. For example, the processing module analyzes, based on one or more of the query request, the fact base info associated with the identified domain, and the further content to produce one or more of updated fact base info (e.g., new knowledge), the updated preliminary occurrence answer (e.g., likelihood of occurrence), and an associated answer quality level. The analyzing may include reasoning the further content with the fact base to produce the updated fact base info and the preliminary answer to include the likelihood of the occurrence.

When the updated answer quality level is favorable, the method continues at step 730 where the processing module issues a query response to the request are that predicts the likelihood of the occurrence. The issuing includes one or more of analyzing the preliminary answers in accordance with the query requirements and the rules to generate the updated quality level, generating the query response to include the answer associated with favorable quality level, and sending the query response to the requester

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 11A is a schematic block diagram of another embodiment of a computing system that includes medical content sources 740, the artificial intelligence (AI) server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The medical content sources 740 includes the content sources 16-1 through 16-N of FIG. 1. In particular, content sources associated with medical information including one or more of anonymous medical records, family medical records, individual medical records, dated with regards to emerging medical scenarios globally and/or by region, DNA-based illness propensity data, anatomy data, symptoms and illnesses data, social media data (e.g., illnesses people are talking about by region), etc. The AI server 20-1 includes the processing module 50-1 of FIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processing module 50-1 includes the collections module 120 of FIG. 4A, the identigen entigen intelligence (IEI) module 122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally, an embodiment of this invention provides a solution to produce a response to a query regarding diagnosing an illness.

In an example of operation of the responding to the query, the query module 124 interprets a received query request 136 to produce query requirements. The interpreting includes one or more of determining content requirements, determining source requirements, determining answer timing requirements, and identifying at least one domain associated with the query request 136. For example, the query module 124 determines the content requirements to include facts that can lead to the diagnosis of the illness, determines the source requirements to include the medical content sources 740, determines the answer timing requirements to include a timeframe associated with the diagnosed illness, and identifies medical diagnosis as the domain when receiving the query request 136 that includes a question “what illness could patient X have with these [symptoms]?”

Having produced the query requirements, the query module 124 issues at least one of an IEI request 244 and a collections request 132 based on the query request 136. For example, the query module 124 generates the IEI request 244 and sends the IEI request 244 to the IEI module 122 when the source requirements suggest that the IEI module 122 is able to provide an immediate response. As another example, the query module 124 generates the collections request 132 and sends the collections request 132 to the collections module 120 when the source requirements suggest that a future time frame is associated with the query request 136 and more content is required. For instance, the query module 124 issues the collections request 132 to the collections module 122 facilitate collecting content over the next few minutes associated with a typical illness of the query request 136 and subsequently issues the IEI request 244 to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEI request 244 to produce human expressions that include question content and question information. The formatting includes analyzing the IEI request 244 for recognizable human expressions of question content and question information in accordance with rules and fact base information 600 (e.g., facts pertaining to medical symptoms and associated illnesses) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEI processing” to the human expressions to produce one or more of new knowledge, a preliminary answer, and an answer quality level associated with the preliminary answer. The IEI processing includes identifying permutations of identigens, reducing the permutations in accordance with the rules, mapping the reduced permutations of identigens to entigens to generate knowledge, processing the knowledge in accordance with the fact base (e.g., fact base info 600) to produce the preliminary answer, and generating the answer quality level based on the preliminary answer and the request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issues a collections request 132 to the collections module 120 to gather more content to produce knowledge to enable a desired favorable quality level of the answer. The issuing includes generating the collections request 132 based on one or more of the IEI requests 244, the preliminary answer, elements of the fact base information 600 (e.g., the present knowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests 132 to produce content requirements. The interpreting includes one or more of determining content selection requirements, determining source selection requirements, and determining content acquisition timing requirements. For example, the collections module 120 determines the source selection requirements to include selecting the content sources 16-1 through 16-N of the medical content sources 740, determines the content selection requirements to include content associated with the symptoms (e.g., symptoms and related illnesses), and determines the content acquisition timing requirements to include a time span for collection if any (e.g., immediately within the next few minutes for medical records since a satisfactory number of medical records should be available to support the present diagnosis query).

Having produced the content requirements, the collections module 120 issues a plurality of content requests 126 to a plurality of content sources identified by the content requirements (e.g., to the content sources 16-1 through 16-N). For example, the collections module 120 identifies the plurality of content sources, generates the content requests based on the content requirements, and sends the plurality of content requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collections module 120 interprets a plurality of content responses 128 to determine whether a response quality level is favorable. The interpreting includes analyzing the plurality of content responses 128 to produce an estimated response quality level, and indicating a favorable response quality level when the estimated response quality level compares favorably to a minimum response quality threshold level (e.g., greater than). When the response quality level is favorable, the collections module 120 issues a collections response 134 to the IEI module 122, where the collections response 134 includes further content. For example, the collections module 120 generates the collections response 134 to include the further content and the estimated response quality level, and sends the collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more of the IEI request 244 and the fact base information 600 to produce one or more of updated fact base information (e.g., new knowledge for storage in the SS memory 96) and a preliminary answer with an associated preliminary answer quality level. For example, the IEI module 122 reasons the further content with the fact base information 600 to produce the preliminary answer which predicts the illness for the symptoms. When the answer quality level is favorable, the IEI module 122 issues an IEI response 246 to the query module 124 where the IEI response 246 includes the preliminary answer associated with a favorable answer quality level. The query module 124 interprets the received answer to produce a quality level of the received answer. For example, the query module 124 analyzes the preliminary answer in accordance with the query requirements and the rules to generate the quality level of the received answer. When the quality level of the received answer is favorable, the query module 124 issues a query response 140 to the user device 12-1, where the query response 140 includes the answer associated with the favorable quality level of the answer.

FIG. 11B is a data flow diagram for providing an answer to a question within a computing system. The data flow diagram includes the IEI module 122 of FIG. 11A and fact base information 600. The fact base info 600 includes a plurality of domain D2-DN groupings tables 750. Each groupings table 750 includes multiple fields including fields for a group (GRP) identifier (ID) 586, word strings 588, identigen (IDN) string 626, and an entigen (ENI) 628. For instance, the groupings tables 750 includes word strings and identifiers associated with symptoms tied to illnesses.

As an example of operation of providing an answer to a query, the IEI module 122 interprets the IEI request 244, facilitates obtaining the fact base information 600, and generates the preliminary answer 246 based on the rules 316. For example, the IEI module 122 generates the preliminary answer to indicate that “most likely illness for patient X with these [symptoms] is [illness], next most likely illness for patient X with these [symptoms] is [illness], etc.”, when the domain D1-DN groupings tables are affiliated with relevant symptoms and associated illnesses. For instance, the IEI module 122 diagnoses the symptoms A3, A4, B3 to be associated with illness X1 first and illness Z1 second and illness Y1 third based inference of fact-based 600 considering a sheer number of common symptoms tied to associated illnesses. Alternatively, the IEI module 122 may predict misdiagnosis based on previous symptoms tied to incorrect root cause illnesses of previous diagnosis.

FIG. 11C is a logic diagram of an embodiment of a method for providing an answer to a question within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, 11A-11B, and also FIG. 11C. The method includes step 760 where a processing module of one or more processing modules of one or more computing devices of the computing system interprets a received query request from a requester to produce query requirements with regards to illness diagnosis. The interpreting includes one or more of determining content requirements, (e.g., to gather symptoms related to illnesses), determining source requirements, determining answer timing requirements, and identifying a domain associated with the query request.

The method continues at step 762 where the processing module IEI processes human expressions of the received query request based on a fact base generated from previous content to produce a preliminary illness diagnosis answer. The processing may include formatting portions of the query request in accordance with formatting rules to produce recognizable human expressions of content and question information. For example, the processing module produces the question information to include a request to determine the illness diagnosis (e.g., identifying symptoms associated with illnesses). The processing may further include identifying permutations of identigens within the human expressions, reducing the permutations, mapping the reduced permutations to entigens to produce knowledge, processing the knowledge in accordance with a fact base to produce the preliminary answer, and generating an answer quality level associated with the preliminary answer. For instance, the processing module generates a relatively low answer quality level when the question relates to gathering information over a subsequent time frame such that more content must be gathered to produce an answer associated with a higher and more favorable answer quality level (e.g., look for recent medical cases with similar symptoms that have been successfully diagnosed as particular illnesses).

When the answer quality level is unfavorable, the method continues at step 764 where the processing module generates content requirements. The generating of the content requirements includes determining, based on one or more of the query requirements, preliminary answer, and the answer quality level, one or more of content selection requirements, source selection requirements, and acquisition timing requirements.

The method continues at step 768 where the processing module obtains further content from a plurality of medical content sources based on the content requirements. For example, the processing module identifies the plurality of medical content sources, generates content requests based on the content requirements, and sends the plurality of content requests to the plurality of identified medical content sources, analyzes a plurality of content responses to produce an estimated quality level, indicates favorable quality level when the estimated quality level compares favorably to a minimum quality threshold level, and indicates unfavorable quality level to facilitate collecting more content when the estimated quality level compares unfavorably to the minimum quality threshold level.

The method continues at step 770 where the processing module IEI processes human expressions of the further content based on the fact base to produce an updated preliminary illness diagnosis answer that identifies the likely illness for the symptoms. For example, the processing module analyzes, based on one or more of the query request, the fact base info associated with the identified domain, and the further content to produce one or more of updated fact base info (e.g., new knowledge), the updated preliminary illness diagnosis answer (e.g., likelihood of an illness), and an associated answer quality level. The analyzing may include reasoning the further content with the fact base to produce the updated fact base info and the preliminary illness diagnosis answer to include the likelihood of the illness.

When the updated answer quality level is favorable, the method continues at step 772 where the processing module issues a query response to the request are that predicts the likelihood of the illness. The issuing includes one or more of analyzing the preliminary illness diagnosis answers in accordance with the query requirements and the rules to generate the updated quality level, generating the query response to include the illness diagnosis answer associated with favorable quality level, and sending the query response to the requester

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 12A is a schematic block diagram of another embodiment of a computing system that includes a plurality of domain 1 (D1) through domain N (DN) content sources 790, a corresponding plurality of the artificial intelligence (AI) servers 20-1 through 20-N of FIG. 1, and the user device 12-1 of FIG. 1. Each of the domain 1-N content sources 790 includes content sources 16-1 through 16-N of FIG. 1. In particular, each of the AI servers 20-1 through 20-N is associated with a corresponding domain D1-DN of content sources 790. Each of the AI servers 20-1 through 20-N includes the processing module 50-1 of FIG. 2 and the solid state (SS) memory 96 of FIG. 2, where each SS memory 96 is utilized to store associated domain knowledge. For instance, the SS memory 96 of the AI server 20-1 is utilized to store domain 1 fact base information 792, the AI server 20-2 is utilized to store domain 2 fact base information 792, etc. Each processing module 50-1 includes the collections module 120 of FIG. 4A, the identigen entigen intelligence (IEI) module 122 of FIG. 4A, and the query module 124 of FIG. 4A. Generally, an embodiment of this invention provides a solution to obtain alternate domain knowledge to produce a query response.

In a first example of operation of the computing system include obtaining of the alternate domain knowledge to produce the query response, the query module 124 interprets a received query request 136 to produce query requirements. The interpreting includes one or more of determining content requirements, determining source requirements, determining answer timing requirements, and identifying an alternate domain when the alternate domain is associated with the query request 136 and disassociated with a domain of the query module 124 (e.g., of the particular AI server). For example, the query module 124 of the AI server 20-1 identifies the alternate domain when the domain of the query request 136 is not the first domain.

Having produced the query requirements, the query module 124 issues an IEI request 244 to the IEI module 122 of the AI server 20-1 based on the query request 136. The IEI module 122 formats the IEI request 244 to produce human expressions that include question content and question information. The formatting includes analyzing the IEI request 244 for recognizable human expressions of question content and question information in accordance with rules and D1 fact base information 792 (e.g., facts pertaining to at least the first domain) obtained from the SS memory 96 of the AI server 20-1.

Having produced the human expressions, the IEI module 122 applies “IEI processing” to the human expressions to produce one or more of new knowledge, a preliminary answer, and an answer quality level associated with the preliminary answer. The IEI processing includes identifying permutations of identigens, reducing the permutations in accordance with the rules, mapping the reduced permutations of identigens to entigens to generate knowledge, processing the knowledge in accordance with the D1 fact base info 792 to produce the preliminary answer, and generating the answer quality level based on the preliminary answer and the request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issues an alternate IEI request 794 to two one or more other IEI modules 122 of other AI servers together gather more content and/or to produce knowledge to enable a desired favorable quality level of the answer. The issuing includes generating the alternate IEI request 794 based on one or more of the IEI request 244, the preliminary answer, elements of the D1 fact base information 792 (e.g., the present knowledge base of the first domain), and the answer quality level. The issuing further includes selecting the one or more other IEI modules 122, where the selecting may be based on one or more of a predetermination, a list of domains versus identifiers of the IEI modules 122, the identified alternate domain associated with the query request 136, the IEI request 244, the preliminary answer, and the answer quality level.

At least one of the one or more other IEI modules 122 issues an alternate IEI response 796 to the IEI module 122, where the alternate IEI response 796 includes one or more of further content, further knowledge, and an alternate answer. For example, the IEI module 122 of the AI server 20-2 generates the alternate IEI response 796 to include knowledge extracted from the D2 fact base information 792.

The IEI module 122 interprets the alternate IEI response 796 and applies IEI processing to one or more of the further content, further knowledge, and alternate answer in light of the preliminary answer to produce one or more of updated fact-based information (e.g., new knowledge of the alternate domain) and an updated preliminary answer with an updated answer quality level. When the updated answer quality level is favorable, the IEI module 122 issues an IEI response 246 to the query module 124 where the IEI response 246 includes the updated answer associated with a favorable updated answer quality level. The query module 124 interprets the received updated answer to produce a quality level of the received answer. For example, the query module 124 analyzes the updated answer in accordance with the query requirements and the rules to generate the quality level of the received answer. When the quality level of the received answer is favorable, the query module 124 issues a query response 140 to the user device 12-1, where the query response 140 includes the answer associated with the favorable quality level of the answer for the alternate domain.

Alternatively, or in addition to, the IEI module 122 of each of the AI servers 20-1 through 20-N may determine to update knowledge related to a multitude of other domains not typically associated with the particular AI server of the IEI module 122. The determining may be based on one or more of a predetermined schedule, in response to a request to update, upon realizing that the alternate domain exists, after receiving a given number of queries related to the other domain, and after producing a threshold number of preliminary answer is associated with unfavorable quality levels. When determining to update the knowledge, the IEI module 122 exchanges alternate IEI requests 794 and alternate IEI responses 796 with a multitude of other IEI modules 122 corresponding to the multitude of other domains. When receiving each IEI response 796, the IEI module 122 applies IEI processing to generate the updated knowledge for storage in an associated SS memory 96.

In a second example of operation of the computing system, the query includes a question: “how many races did Mario Andretti win in his career”? The plurality of AI servers 20-1 through 20-N include a knowledge database associated with Mario Andretti, a general sports knowledge database, an auto racing database, a database of sports statistics, a database of media articles, a database of Mario Andretti interview content, etc.

The second example of operation includes the IEI module 122 receiving the query via the IEI request 244 from the query module 124 and the query request 136 from the user device 12-1. Having received the query, the IEI module 122 utilizes at least two of the knowledge databases to produce two preliminary answers and compares the two preliminary answers to determine whether they match. Generally, when the preliminary answers match, the IEI module 122 utilizes either preliminary answer as a final answer. However, when they don't match, the IEI module 122 indicates that the two preliminary answers are divergent (e.g., unfavorably different) and identifies inconsistencies between the two preliminary answers to resolve the inconsistencies in a way to provide an acceptable final answer for issuing, utilizing the IEI response 246 via the query module 124 as the query response 140, to the user device 12-1.

The inconsistencies include a first type where information present in one knowledge database and not present in another knowledge database. As an instance of the first type, a first knowledge database indicates that Mario Andretti won three races at the Watkins Glen track and a second knowledge database does not include information for Watkins Glen.

A second type of inconsistency includes information from one knowledge database that conflicts with information in another knowledge database. As an instance of the second type, the first knowledge database indicates that Mario Andretti won three races at the Watkins Glen track and the second knowledge database indicates that Mario Andretti won 2 races at Watkins Glen.

A third type of inconsistency includes information present in one knowledge database and not present in the majority of the other knowledge databases. As an instance of the third type, the first knowledge database indicates that Mario Andretti won three races at the Watkins Glen track and ten other knowledge databases of a total of 15 knowledge databases do not include information for Watkins Glen.

A fourth type of inconsistency includes information in one knowledge database that conflicts with information in a majority of other knowledge databases. As an instance of the fourth type, the first knowledge database indicates that Mario Andretti won three races at the Watkins Glen track and ten other knowledge databases of a total of 15 knowledge databases indicate that Mario Andretti won 2 races at Watkins Glen.

The IEI module 122 resolves the inconsistencies between the first and second preliminary answers by a variety of approaches. A first approach includes updating a first knowledge database with knowledge from a second knowledge database to facilitate regenerating a better first preliminary answer. For instance, when the identified inconsistency includes the first type (e.g., non-conflicting but different data in the different knowledge databases), the IEI module 122 receives the alternate IEI response 796 that includes knowledge of the second knowledge database, integrates the knowledge with the first knowledge database, and re-processes the question to produce the better first preliminary answer as the final answer (e.g., combining nonconflicting race statistics from the second knowledge database with race statistics of the first knowledge database).

A second approach includes selecting a preliminary answer disassociated from inconsistencies. For instance, when the identified inconsistency includes the second type (e.g., the preliminary answer conflicts with at least one other preliminary answer), the IEI module 122 selects a portion of a preliminary answer that does not conflict with corresponding portions of other preliminary answers to produce the final answer (e.g., selecting race statistics of a knowledge database that does not conflict with race statistics of other knowledge databases).

A third approach includes selecting a preliminary answer that is disassociated from knowledge available from the majority of the knowledge databases. For instance, when the identified its consistency includes the third type (e.g., only one knowledge database provides desired statistics and the majority of knowledge bases do not), the IEI module 122 selects the preliminary answer associated with one knowledge database and not in the majority of the other knowledge databases to produce the final answer (e.g., selecting race statistics of a knowledge database but is not included with the majority of the other knowledge databases).

A fourth approach includes selecting knowledge from any knowledge database of a majority of the knowledge databases that have a consistent preliminary answer unlike at least one knowledge database associated with a conflicting preliminary answer. For instance, when the inconsistency type includes the fourth type (e.g., one conflicting knowledge database conflicts with the majority of the other knowledge databases), the IEI module 122 selects a preliminary answer associated with the majority of the knowledge databases to produce the final answer (e.g., selecting race statistics that agree amongst the majority of the knowledge databases but conflicts with at least one knowledge database).

FIG. 12B is a logic diagram of an embodiment of a method for utilizing multiple knowledge bases to produce a query response within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, 12A, and also FIG. 12B. The method includes step 800 where a processing module of one or more processing modules of one or more computing devices of the computing system determines a set of identigens for each word of at least some words of a string of words of the query to produce a plurality of sets of identigens. Each identigen of the set of identigens is a different meaning of a corresponding word.

The determining the set of identigens for each word of the at least some words of the string of words of the query to produce the plurality of sets of identigens includes mapping a word of the at least some words of the string of words of the query to a set of entries of an identigen dictionary (e.g., listing of words and identigens) to identify the set of identigens. Each set of identigens includes one or more identigens.

As an example of the determining the set of identigens, when the string of words includes “how many races did Mario Andretti win in his career”? The processing module produces, for the words “how many”, a first set of identigens that includes one characteristic identigen associated with a numeric characteristic query. The processing module produces, for the word “races”, a second set of action identigens to include numerous types of races (e.g., an object identigen associated with the word “race” as a thing, an action identigen associated with the word “race” as in “to race”).

The processing module produces, for the words “Mario Andretti”, a third set of object identigens that includes one identigen for the one and only Mario Andretti and another identigen for another Mario Andretti that lives in Rome. The processing module produces, for the word “win”, a fourth set of identigens that includes an action identigen (e.g., to win a race) and an object identigen (e.g., a won race).

The method continues at step 802 where the processing module interprets, based on a first knowledge database, the plurality of sets of identigens to produce a first entigen group. Each entigen of the first entigen group corresponds to a selected identigen of one of the plurality of sets of identigens that represents a first most likely meaning (e.g., a truest interpretation of the word is used) of a corresponding word of the at least some of the words of the string of words. The first entigen group is a first most likely meaning of the string of words and the first knowledge database includes a first plurality of records that link words (e.g., via entigens) having a first connected meaning.

The records link entigens, where each entigen represents one of an object, an action, or a characteristic (OCA). The records further link multiple entigens together to facilitate a representation of the connected meaning. The connected meaning includes at least one of describes, acts on, is a, belongs to, did, did too, etc. For instance, an entigen representing the meaning of “eats” is linked to indicate connected meaning with another entigen representing “fruit.”

Each entigen is independent of language since it represents the OCA (meaning). The connected meaning further represents a relationship between multiple words in different languages that mean the same thing and multiple identigens (one per language), but they all have connected meaning to link to common entigens representing that meaning.

The interpreting the plurality of sets of identigens to produce the first entigen group includes a series of steps. A first step includes identifying alternative sequences of identigens for the plurality of sets of identigens (e.g., permutations of one identigen per set of identigens). An alternative sequence of identigens includes one identigen of each set of identigens of the plurality of sets of identigens such that each alternative sequence of identigens includes a unique combination of identigens.

A second step includes ranking the alternative sequences of identigens based on the first knowledge database (e.g., more favorable ranking when favorably linked entigens of the first knowledge database correspond to the alternative sequence of identigens) to identify a first alternative sequence of identigens that corresponds to a first most favorable identigen sequence ranking (e.g., the first alternative sequence of identigens having a most favorable ranking). An identigen of the first alternative sequence of identigens corresponds to the selected identigen of one of the plurality of sets of identigens that represents the first most likely meaning of the corresponding word of the at least some of the words of the string of words.

A third step includes establishing the first entigen group based on the first alternative sequence of identigens. For example, the identifiers of the first alternative sequence of identigens are copied to produce the first entigen group.

As an example of the producing of the first entigen group, the processing module includes the characteristic identigen associated with the numeric characteristic query (e.g., words: “how many”), the object identigen associated with the “thing” called races, the object identigen associated with the one and only Mario Andretti, and the action identigen associated with winning a race.

The method continues at step 804 where the processing module generates a first preliminary query response (e.g., a set of preliminary answers to a question of the query, a request for clarifying information when the query is ambiguous, etc.) based on the first entigen group. As an example of the generating of the first preliminary query response, the processing module produces the first preliminary query response to include a list of races that Mario Andretti has known to win from records of the first knowledge database.

The generating of the first preliminary query response includes a series of steps. A first step includes locating a portion of the first knowledge database associated with the query based on the ranking of the alternative sequences of identigens. For instance, the processing module accesses the first knowledge database to recover records and matches the portion of the first knowledge base to at least some entigens of the first entigen group.

The second step includes applying query response logic (e.g., inferences, deductions, rationalizations) to the first entigen group and to entigens of the portion of the first knowledge database utilizing relationships between the entigens of the portion of the first knowledge database to produce the first preliminary query response. For instance, the processing module generates the first preliminary query response utilizing inference to include an entigen of the portion of the first knowledge base that is missing from but related to the entigens of the first entigen group.

The method continues at step 806 where the processing module interprets, based on a second knowledge database, the plurality of sets of identigens to produce a second entigen group. For example, the processing module accesses the second knowledge database to recover records that include one or more of a portion of the second knowledge database and the second entigen group (e.g., when the processing module requests that another processing module produce the second entigen group). For instance, the processing module utilizes the recovered records from the second knowledge database that includes an entigen associated with the meaning of “races” that further pinpoints auto races and in particular, auto races that were associated with events that Mario Andretti participated in as a driver.

Each entigen of the second entigen group corresponds to a selected identigen of one of the plurality of sets of identigens that represents a second most likely meaning of a corresponding word of the at least some of the words of the string of words. The second entigen group is a second most likely meaning of the string of words (e.g., from the perspective of the second knowledge database). The second knowledge database includes a second plurality of records that link words having a second connected meaning.

The method continues at step 808 where the processing module generates a second preliminary query response based on the second entigen group. For example, the processing module generates the second preliminary query response utilizing deduction to include two entigens of the portion of the second knowledge base are related to the entigens of the first entigen group. As another example of the generating of the second preliminary query response, the processing module produces the second preliminary query response to include a list of races that Mario Andretti has known to win from records of the second knowledge database.

The method continues at step 810 where the processing module compares the first and second preliminary query responses. The comparing includes one or more of finding non-conflicting information, finding conflicting information, and determining a quality level. For example, the processing module determines a first quality level of the first preliminary query response utilizing a likelihood assessment based on previous accuracy of query responses to associate queries. For instance, a more favorable quality level indicates that, more often than not, similar queries and query responses were found to have a more favorable level of correctness.

The comparing of the first and second preliminary query responses further includes determining a second quality level of the second preliminary query response in a similar fashion as described above. Having compared the first and second preliminary query responses, the processing module indicates that the first and second preliminary query responses are divergent (e.g., unfavorably different) by one or more of a variety of further approaches.

A first approach includes detecting that the first quality level of the first preliminary query response is less than a minimum quality threshold level (e.g., set by previous quality results) and that a second quality level of the second preliminary query response is greater than the minimum quality threshold level. For example, the first preliminary query response is unacceptable and the second preliminary query response is acceptable. For instance, the first preliminary query response has an acceptable list of races won while the second preliminary response includes conflicting race information.

A second approach includes detecting that the first quality level of the first preliminary query response is greater than the minimum quality threshold level (e.g., set by previous quality results) and that the second quality level of the second preliminary query response is less than the minimum quality threshold level. For example, the first preliminary query response is acceptable and the second preliminary query response is unacceptable. For instance, the first preliminary query response includes conflicting race information while the second preliminary response includes an acceptable list of races won.

A third approach includes detecting that the first quality level of the first preliminary query response is less than the minimum quality threshold level and that the second quality level of the second preliminary query response is less than the minimum quality threshold level. For example, both the first and the second preliminary query responses are unacceptable by themselves. For example, both preliminary query responses include conflicting race information.

A fourth approach includes detecting that the first and second preliminary query responses are different. For example, at least one aspect (e.g., part of an answer) of one of the first or second preliminary query responses is not substantially the same as a corresponding aspect of the other preliminary query response. For instance, the first preliminary query response includes race results for Watkins Glen but not for Long Beach while the second preliminary query response does not include the results for Watkins Glen but does include the results for Long Beach.

When the first preliminary query response and the second preliminary query response are divergent, the method continues at step 812 where the processing module resolves inconsistencies (e.g., differences in content, differences in quality levels, other differences) between the first and second preliminary query responses to produce a final query response. Having produced the final query response, the processing module facilitates distribution of the final query response to a final query response recipient.

The resolving of the inconsistencies between the first and second preliminary query responses to produce the final query response includes one of a variety of approaches. When the first quality level of the first preliminary query response is less than the minimum quality threshold level and the second quality level of the second preliminary query response is less than the minimum quality threshold level, a first approach includes a series of steps.

A first step includes accessing the second knowledge database to obtain a portion of the second knowledge database. For instance, the processing module receives the portion of the second knowledge database (e.g., from a storage facility not associated with the processing module).

A second step includes combining the portion of the second knowledge database with the first knowledge database to produce an updated first knowledge database. For instance, the processing module extracts incremental entigens of the portion of the second knowledge base and integrates the incremental entigens with the first entigen group to produce the updated first knowledge base.

A third step include processing the query using the updated first knowledge database to produce the final query response. For example, the processing module generates the final query response utilizing deduction to include three entigens of the portion of the updated knowledge base that are related (e.g., including, link to) to the incremental entigens. For instance, the processing module generates the final query response to include a composite of likely races that Mario Andretti won as indicated by knowledge of the first knowledge base and the second knowledge base.

When the first quality level of the first preliminary query response is less than the minimum quality threshold level and the second quality level of the second preliminary query response is greater than the minimum quality threshold level, a second approach includes selecting the second preliminary query response as the final query response. For example, the processing module generates the final query response to include words that are associated with the second preliminary query response. For instance, the processing module generates the final query response to only include races won by Mario Andretti as indicated in the second knowledge base.

When detecting that the first and second preliminary query responses are different, a third approach includes one sub-approach of a variety of sub-approaches. A first sub-approach includes selecting one of the first preliminary query response and the second preliminary query response as the final query response based on the first and second quality levels. For example, the processing module selects a preliminary query response associated with a quality level that is more favorable than the other preliminary query response to generate the final query response. For instance, the processing module selects races won by Mario Andretti as indicated by the first knowledge database when those races won are more consistent with majority of the other knowledge databases (e.g., higher quality level than the quality level associated with the second knowledge database).

A second sub-approach include combining a first portion of the first preliminary query response and a second portion of the second preliminary query response as the final query response when a quality level of the final query response is more favorable than both of the first and second quality levels. For example, the processing module identifies the first portion of the first preliminary query response that has a favorable quality level, identifies the second portion of the second preliminary query response that has a favorable quality level, and generates the final query response utilizing the two favorable portions of the preliminary query responses. For instance, the processing module utilizes a first sub-list of races won by Mario Andretti from the first knowledge base and a second sub-list of races won by Mario Andretti from the second knowledge base to produce the final query response.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 13A is a data flow diagram for optimizing identification of permutations of element groupings within a computing system that includes another embodiment of the element identification module 302 of FIG. 5E and another embodiment of the IEI control module 308 of FIG. 5E. The element identification module 302 includes another embodiment of the element matching module 400 of FIG. 6A and another embodiment of the element grouping module 402 of FIG. 6A. The other embodiment of the element identification module 300 and to generally functions to generate an optimized set of permutations of identified elements.

In an example of operation of the generating of the optimized set of permutations of identified elements, when matching elements of the formatted content 314, the element matching module 400 generates matched elements 412 based on the element list 332. For example, the element matching module 400 matches a received element of the formatted content 314 to an element of the element list 332 and outputs the matched elements 412 to includes an identifier of the matched element. To facilitate reduction of the necessary processing, the element matching module 400 further issues optimization triggers 820 to the IEI control module 308 based on the matched elements 412 and the formatted content 314. The optimization triggers 820 includes a hypothesis token that may include one or more of patterns of word types including verbs, adjectives, nouns, adverbs, etc., language indicators, dialect indicators, domain indicators, and matched elements 412.

The element grouping module 402 analyzes the matched elements 412 in accordance with the element rules 318 and optimization guidance 822 received from the IEI control module 308 to reduce unnecessary processing when outputting the identified element information 340. The optimization guidance 822 includes one or more of element rule modifiers, word text skip when generating permutations of groupings of words (e.g., skip adjectives), and one or more domain indicators. The outputting may include comparing a groupings aspect of the identified element information 340 (e.g., for each fermentation of groups of elements of possible interpretations), and generating validated groupings information to include identification of valid permutations that align with the groupings list.

FIG. 13B is a logic diagram of an embodiment of a method for generating an optimized set of permutations of identified elements within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, 13A, and also FIG. 13B. The method includes step 830 where, when matching elements of formatted content, a processing module of one or more processing modules of one or more computing devices of the computing system generates matched elements. The generating includes matching a received element to an element of an element list and outputting the matched elements to include an identifier of the matched element.

The method continues at step 832 where the processing module generates optimization triggers based on the matched elements. For example, the processing module analyzes patterns (e.g., word types, ordering, frequency) of the matched elements to generate a hypothesis token to include the optimization triggers. The method continues at step 834 where the processing module generates optimization guidance based on the optimization triggers and an optimization approach. For example, the processing module selects the optimization approach (e.g., predetermined, lookup, received, determined based on a domain) and analyzes the optimization triggers using the optimization approach to produce the optimization guidance.

The method continues at step 836 the processing module generates an optimized set of permutations of identified elements from the matched elements in accordance with the optimization guidance. The generating includes one or more of comparing a groupings aspect of the matched elements (e.g., for each fermentation of groups of elements of possible interpretations) and generating validated groupings information (e.g., reduced permutations) to include identifications of valid permutations that align with a groupings list and element rules.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

FIG. 14A is a schematic block diagram of another embodiment of a computing system that includes sentiment content sources 840, the artificial intelligence (AI) server 20-1 of FIG. 1, and the user device 12-1 of FIG. 1. The sentiment content sources 840 includes the content sources 16-1 through 16-N of FIG. 1. In particular, content sources associated with sentiment information including one or more of chained statements that infer sentiments, related facts that infer sentiments, sentiments expressed by individuals, sentiments expressed by groups, sentiments associated with demographics, sentiments associated with various domains, etc. The AI server 20-1 includes the processing module 50-1 of FIG. 2 and the solid state (SS) memory 96 of FIG. 2. The processing module 50-1 includes the collections module 120 of FIG. 4A, the identigen entigen intelligence (IEI) module 122 of FIG. 4A, and the query module 124 of FIG. 4A.

Generally, an embodiment of this invention provides a solution to identify a digital representation of a human reaction. The digital representation of a human reaction includes digital representations of responses to one or more environmental factors (e.g., physical surroundings, physical being status, communication with other entities, interfacing with a computing system, internal thoughts, etc.). The human reactions include physical reactions (e.g., crying, laughing, frowning, etc.) and mental reactions (e.g., sentiment, modified thoughts, new thoughts, emotional thoughts, etc.).

First and second embodiments of this invention are discussed in greater detail with reference to FIGS. 14A-14B, where sentiments are used as examples of the digital representations of a human reaction. A third embodiment of this invention is discussed in greater detail with reference to FIGS. 14C-F, where sentiments are used as examples of the digital representations of a human reaction.

In an example of the first embodiment of the identifying of the digital representation of a human reaction, the query module 124 interprets a received query request 136 to produce query requirements. The interpreting includes one or more of determining content requirements, determining source requirements, determining answer timing requirements, and identifying at least one domain associated with the query request 136. For example, the query module 124 determines the content requirements to include facts that can lead to the identification of the digital representation of a human reaction (e.g., a sentiment in this example), determines the source requirements to include the sentiment content sources 840, determines the answer timing requirements to include a timeframe associated with the expression or evolution of the sentiment, and identifies sentiment as the domain when receiving the query request 136 that includes a question “what sentiments are inferred by this [passage] by John Smith?”

Having produced the query requirements, the query module 124 issues at least one of an IEI request 244 and a collections request 132 based on the query request 136. For example, the query module 124 generates the IEI request 244 and sends the IEI request 244 to the IEI module 122 when the source requirements suggest that the IEI module 122 is able to provide an immediate response (e.g., identifying sentiments within a passage included with the query request 136). As another example, the query module 124 generates the collections request 132 and sends the collections request 132 to the collections module 120 when the source requirements suggest that a shift in sentiment over time may be is associated with the query request 136 and more content is required. For instance, the query module 124 issues the collections request 132 to the collections module 122 facilitate collecting content over the next week associated with public sentiment related to a topic of discussion in the public of the query request 136 and subsequently issues the IEI request 244 to the IEI module 122 to generate the response to the query.

When receiving the IEI request 244, the IEI module 122 formats the IEI request 244 to produce human expressions that include question content and question information. The formatting includes analyzing the IEI request 244 for recognizable human expressions of question content and question information in accordance with rules and fact base information 600 (e.g., facts pertaining to expressions of sentiment) obtained from the SS memory 96.

Having produced the human expressions, the IEI module 122 applies “IEI processing” to the human expressions to produce one or more of new knowledge, a preliminary answer, and an answer quality level associated with the preliminary answer. The IEI processing includes identifying permutations of identigens, reducing the permutations in accordance with the rules, mapping the reduced permutations of identigens to entigens to generate knowledge, processing the knowledge in accordance with the fact base (e.g., fact base info 600) to produce the preliminary answer, and generating the answer quality level based on the preliminary answer and the request (e.g., the IEI request 244, the query request 136).

When the answer quality level is unfavorable, the IEI module 122 issues a collections request 132 to the collections module 120 to gather more content to produce knowledge to enable a desired favorable quality level of the answer. The issuing includes generating the collections request 132 based on one or more of the IEI requests 244, the preliminary answer, elements of the fact base information 600 (e.g., the present knowledge base), and the answer quality level.

The collections module 120 interprets one or more collections requests 132 to produce content requirements. The interpreting includes one or more of determining content selection requirements, determining source selection requirements, and determining content acquisition timing requirements. For example, the collections module 120 determines the source selection requirements to include selecting the content sources 16-1 through 16-N of the sentiment content sources 840, determines the content selection requirements to include content associated with the sentiment (e.g., similar expressions of the passage affiliated with sentiments), and determines the content acquisition timing requirements to include a time span for collection if any (e.g., over the next week to detect shifts in sentiment by the public).

Having produced the content requirements, the collections module 120 issues a plurality of content requests 126 to a plurality of content sources identified by the content requirements (e.g., to the content sources 16-1 through 16-N). For example, the collections module 120 identifies the plurality of content sources, generates the content requests based on the content requirements, and sends the plurality of content requests to the identified plurality of content sources.

Having issued the plurality of content requests 126, the collections module 120 interprets a plurality of content responses 128 to determine whether a response quality level is favorable. The interpreting includes analyzing the plurality of content responses 128 to produce an estimated response quality level, and indicating a favorable response quality level when the estimated response quality level compares favorably to a minimum response quality threshold level (e.g., greater than). When the response quality level is favorable, the collections module 120 issues a collections response 134 to the IEI module 122, where the collections response 134 includes further content. For example, the collections module 120 generates the collections response 134 to include the further content and the estimated response quality level, and sends the collections response 134 to the IEI module 122.

The IEI module 122 analyzes the further content based on one or more of the IEI request 244 and the fact base information 600 to produce one or more of updated fact base information (e.g., new knowledge for storage in the SS memory 96) and a preliminary answer with an associated preliminary answer quality level. For example, the IEI module 122 reasons the further content with the fact base information 600 to produce the preliminary answer which identifies the sentiments associated with the passage. When the answer quality level is favorable, the IEI module 122 issues an IEI response 246 to the query module 124 where the IEI response 246 includes the preliminary answer associated with a favorable answer quality level. The query module 124 interprets the received answer to produce a quality level of the received answer. For example, the query module 124 analyzes the preliminary answer in accordance with the query requirements and the rules to generate the quality level of the received answer. When the quality level of the received answer is favorable, the query module 124 issues a query response 140 to the user device 12-1, where the query response 140 includes the answer associated with the favorable quality level of the answer.

In an example of the second embodiment of the identifying of the sentiment of the digital representation of a human reaction, and in particular, the storage of content as incremental knowledge to include sentiment information with regards to the incremental knowledge, the IEI module 122 issues a collections request 130 to the collections module 120, where the collections module 120 issues content requests 126 to the sentiment content sources 840. In response, the collections module 120 receives content responses 128 and issues a collections response 134 to the IEI module 122, where the collections response 134 includes a string of words for ingestion to create the incremental knowledge for storage with the associated sentiment information.

Having received the string of words, the IEI module 122 determines a set of identigens for each word of at least some words of the string of words (e.g., the new content) to produce a plurality of sets of identigens. Each identigen of the set of identigens is a different meaning of a corresponding word. For instance, the IEI module 122 maps each word to an identigen dictionary to identify a corresponding identigen. Each set of identigens includes one or more identigen.

Having produced the plurality of sets of identigens, the IEI module 122 interprets, based on the knowledge database (e.g., fact base information 600 from the SS memory 96) the plurality of sets of identigens to produce an entigen group. Each entigen of the entigen group corresponds to a selected identigen of one of the plurality of sets of identigens that represents a most likely meaning of a corresponding word of the at least some of the words of the string of words. The entigen group is a most likely meaning of the string of words. The knowledge database includes a plurality of records that link words (e.g., via entigens) having a connected meaning.

The interpreting of the plurality of sets of identigens to produce the entigen group includes a series of steps. A first step includes identifying alternative sequences of identigens of the plurality of sets of identigens. Each alternative sequence is associated with one identigen of the set identigens for each word of the string of words. A second step includes ranking the alternative sequences of identigens based on the knowledge database to identify a most likely alternative sequence of identigens associated with the most likely meaning of string of words to produce the entigen group. For instance, the IEI module 122 applies knowledge database aligned entigen sequencing rules to the alternative sequences of identigens to reveal a particular alternative sequence of identigens that uniquely aligns with a favorable meaning of the string of words.

Having produced the entigen group, the IEI module 122 integrates the entigen group into a portion of the knowledge database. The integrating of the entigen grouping to the portion of the knowledge database includes a series of steps. A first step includes locating the portion of the knowledge database based on the entigen group. For example, the IEI module 122 accesses fact base information 600 from the SS memory 96 to identify entigens of the portion of the knowledge database that compare favorably to at least some entigens of the entigen group.

A second step includes linking at least some entigens of the entigen group with existing entigens of the portion of the knowledge database. For example, the IEI module 122 identifies entigens to add, updates linking between entigens, adds new links for additional entigens, and stores the changes as updated fact base information 600 in the SS memory 96.

Having integrated the entigen grouping to the portion of the knowledge database, the IEI module 122 determines whether a subset of entigens of the portion of the knowledge database includes sentiment information. The sentiment information includes one or more sentiment characteristic entigens. The determining whether the subset of entigens of the portion of the knowledge database includes the sentiment information includes a series of steps.

A first step includes locating the subset of entigens of the portion of the knowledge database based on the entigen group. The subset of entigens includes existing entigens of the portion of the knowledge database and at least some entigens of the entigen group. For example, the IEI module 122 accesses the fact base information 600 of the SS memory 96 to locate entigens of the portion of the knowledge database that compare favorably to at least some entigens of the entigen grouping.

A second step includes indicating that the subset of entigens does not include the sentiment information based on one or more sub-steps. A first sub-step includes detecting that the subset of entigens does not include the one or more sentiment characteristic entigens. A second sub-step includes detecting that the subset of entigens are not linked to the one or more sentiment characteristic entigens.

When subset of entigens of the portion of the knowledge database does not include the sentiment information, the IEI module 122 determines the sentiment information for the subset of entigens of the portion of the knowledge database. The determining the sentiment information for the subset of entigens of the portion of the knowledge database includes a series of steps.

A first step includes locating another subset of entigens of the portion of the knowledge database that has a meaning that is associated with the entigen group. For example, the IEI module 122 accesses the fact base information 600 from the SS memory 96 to identify related chained identigens that are associated with words that infer sentiments.

A second step includes extracting the one or more sentiment characteristic entigens from the other subset of entigens to produce the sentiment information. The IEI module 122 extracts the one or more sentiment characteristic entigens based on one or more of identifying related facts that infer sentiments, extracting sentiments expressed by individuals, extracting sentiments expressed by groups, finding sentiments associated with demographics, and locating sentiments associated with various domains. Once located, the IEI module 122 chooses the same sentiment when meanings are similar across a favorable number of the at least one other entigen group or chooses the same sentiment when relationships between entigens of each entigen group are similar across a favorable number of the at least one other entigen group.

Having determined the sentiment information for the subset of entigens, the IEI module 122 modifies the portion of the knowledge database to include the sentiment information for the subset of entigens. The modifying of the portion of the knowledge database to include the sentiment information for the subset of entigens includes a series of steps.

A first step includes generating an incremental sentiment characteristic entigen for each of the one or more sentiment characteristic entigens of the sentiment information. For example, the IEI module 122 extracts sentiment identifiers from each of the one or more sentiment characteristic entigens to produce each incremental sentiment characteristic entigen.

A second step includes linking each incremental sentiment characteristic entigen to at least one entigen of the subset of entigens of the portion of the knowledge database. For example, the IEI module 122 accesses the SS memory 96 with updated fact base information 600 that includes one or more of linking between entigens and new linking for the additional sentiment characteristic entigens for storage in the SS memory 96.)

Having converted the content of the string of words into the incremental knowledge for storage in the knowledge base with the sentiment information, the updated knowledge base may be utilized for subsequent identification of sentiment for a subject new string of words of a sentiment query. An example of identifying sentiment for the subject new string of words of the sentiment query is discussed in further detail with regards to FIG. 14B.

FIG. 14B is a data flow diagram for identifying sentiment within a computing system. The data flow diagram includes the IEI module 122 of FIG. 14A and fact base information 600. The fact base info 600 includes a plurality of sentiment class S1-SN groupings tables 846. Each groupings table 846 includes multiple fields including fields for a group (GRP) identifier (ID) 586, word strings 588, identigen (IDN) string 626, and an entigen (ENI) 628. For instance, the groupings tables 846 includes word strings and identifiers associated with phrases that tie to sentiments

In another example of the first embodiment of the identifying of the sentiment of the digital representation of a human reaction, (e.g., providing an answer to a query), the IEI module 122 interprets the IEI request 244, facilitates obtaining the fact base information 600, and generates the preliminary answer 246 based on the rules 316. For example, the IEI module 122 generates the preliminary answer to include “most likely sentiments inferred by this [passage . . . ] by John Smith are [sentiments]” in response to the IEI request 244 of “what sentiments are inferred by this [passage] by John Smith?” when the sentiment class groupings tables are affiliated with relevant phrases tied to sentiments (e.g., for John Smith and for others). For instance, the IEI module 122 identifies the phrases A1 and A2 to be associated with sentiment X1 first and phrase A1 to be associated with sentiment X7 second based inference of fact-based 600.

FIGS. 14C-E are entigen group diagrams of an embodiment of a knowledge database to illustrate an example of the second embodiment of the identifying of the sentiment of the digital representation of a human reaction. The FIGS. 14C-E represent a portion of the knowledge database to include one or more entigen groups when the knowledge database utilizes a graphical database format. For example, the entigen groups belong to the fact base information 600 stored in the SS memory 96 of FIG. 14A. An entigen group includes entigens associated with a most likely meaning of a phrase of a string of words. Each entigen is stored in the knowledge database utilizing an entigen identifier. When utilizing the graphical database format, an entigen is illustrated as a circle with lines and arrows to indicate connections one or more other entigens.

A sentiment associated with one or more entigens may also be stored in the knowledge base utilizing a variety of approaches. A first approach includes tagging the knowledge database record of the entigen with an associated sentiment (e.g., include an identifier of the sentiment along with the identifier of the entigen). A second approach includes adding a sentiment characteristic entigen (e.g., happy, sad, etc.) to represent a particular sentiment and linking the sentiment characteristic entigen to the one or more entigens.

FIG. 14C illustrates a first step of the example of the second embodiment of the invention, where the processing module 50-1 of FIG. 14A selects an entigen group that is a most likely meaning of a phrase from a knowledge database. The selecting includes identifying an entigen group from the knowledge database that does not have a sentiment. For example, the processing module 50-1 identifies entigen grouping 850-1 that does not have an assigned sentiment, where the associated string of words includes “Jack and Jill are lovers. Jack departed and Jill cried.” While no sentiment has been assigned, potential sentiments include sad or happy. The entigen group includes entigens for a specific Jack (e.g., the one and only on the planet), a specific Jill, an action of departing, a characteristic of lovers, and an action of cried. The graphical database representation identifies via the lines and arrows the linkage between the entigens in accordance with the sequencing of the string of words of the phrase.

As another example, the processing module 50-1 identifies entigen grouping 850-2 that does not have an assigned sentiment, where the associated string of words includes “Jack and Jill are lovers. Jack arrived and Jill cried.” As in the other example, while no sentiment has been assigned, potential sentiments include sad or happy. The method of identifying the entigen group is discussed in greater detail with reference to FIG. 14F.

FIG. 14D illustrates further steps of the example of the second embodiment of the invention, where the processing module 50-1 of FIG. 14A identifies other entigen groups of alternate phrases having the most likely meaning from the knowledge database. The alternate phrases have a similarity to the phrase that has no sentiment assigned and will be helpful to identify sentiment to assign to the entigen group.

For example, the processing module 50-1 identifies entigen group 854-1, where an alternate phrase includes “Bob and Mary are lovers. Bob departed and Mary cried.” The processing module 50-1 also identifies entigen group 854-2, where another alternate phrase includes “Linda and Joe are lovers. Linda departed and Joe was unhappy.”

Having identified the other entigen groups of the alternate phrases, a further step of the example of the second embodiment includes the processing module 50-1 determining a dominant sentiment of the other entigen groups. The determining of the dominant sentiment may be accomplished by a variety of approaches. For instance, the processing module 50-1 selects the sentiment “sad” associated with the majority of the other entigen groups as a dominant sentiment 856-1. Other approaches are discussed in greater detail with reference to FIG. 14F.

Having determined the dominant sentiment, another further step of the example of the second embodiment includes the processing module 50-1 generating a sentiment for the entigen group based on the dominant sentiment. The generating of the sentiment for the entigen group may be accomplished by a variety of approaches. For instance, the processing module 50-1 equates the dominant sentiment 856-1 (e.g., sad) to the sentiment 860-1 (e.g., sad) of the entigen group when a simple majority is desired. Other approaches and more details of the second embodiment of the invention are discussed in greater detail with reference to FIG. 14 F.

FIG. 14E illustrates further steps after the first step of the example of the second embodiment of the invention from FIG. 14C, where the processing module 50-1 of FIG. 14A identifies the other entigen groups of alternate phrases having the most likely meaning from the knowledge database. For example, the processing module 50-1 identifies entigen group 854-11, where an alternate phrase includes “Bob and Mary are lovers. Bob arrived and Mary cried.” The processing module 50-1 also identifies entigen group 854-12, where another alternate phrase includes “Linda and Joe are lovers. Linda arrived and Joe was pleased.”

Having identified the other entigen groups of the alternate phrases, a further step includes the processing module 50-1 determining a dominant sentiment of the other entigen groups. For instance, the processing module 50-1 selects the sentiment “happy” associated with the majority of the other entigen groups as a dominant sentiment 856-11.

Having determined the dominant sentiment, another further step includes the processing module 50-1 generating a sentiment for the entigen group based on the dominant sentiment. For instance, the processing module 50-1 equates the dominant sentiment 856-11 (e.g., happy) to the sentiment 860-11 (e.g., happy) of the entigen group when a simple majority is desired.

FIG. 14F is a logic diagram of the second embodiment of a method for identifying a digital representation of a human reaction within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with FIGS. 1-8D, and also 14C-14E. The method includes step 870 where a processing module of one or more processing modules of one or more computing devices of the computing system selects an entigen group from a knowledge database. The entigen group is a most likely meaning of a phrase of a string of words and an entigen of the entigen group corresponds to a selected identigen from a set of identigens regarding a word of the string of words. The set of identigens represent different meanings of the word.

The selecting the entigen group from the knowledge database includes a variety of selecting approaches. A first selecting approach includes initiating a search of the knowledge database to identify the entigen group when a search timeframe has expired. For instance, the shading the search when a timer expires to trigger the new search.

A second selecting approach includes receiving a digital representation of a human reaction identification request from a requesting entity. For instance, the processing module interprets a query that includes the digital representation of a human reaction identification request.

A third selecting approach includes detecting that the entigen group does not include the digital representation of a human reaction. For example, a search of the knowledge database locates the entigen group that does not have a digital representation of a human reaction included in an entigen record nor does it have a digital representation of a human reaction characteristic attached to any of the entigens of the entigen group. For instance, the search of the knowledge database locates the entigen group associated with the phrase “Bob and Mary are lovers. Bob departed and Mary cried.”

A fourth selecting approach includes detecting that the entigen group includes an incorrect digital representation of a human reaction. For example, the processing module compares the incorrect digital representation of a human reaction to another dominant digital representation of a human reaction associated with alternate entigen groups that have a similar meaning to the entigen group. For instance, nine out often of the other alternate entigen groups indicate that when Bob departs Mary cries because she is sad.

The method continues at step 872 where the processing module identifies other entigen groups of alternate phrases having the most likely meaning from the knowledge database. The alternate phrases are different permutations of the string of words. The identifying the other entigen groups includes a variety of identification approaches.

A first identification approach includes matching the entigen group to a first entigen group of the other entigen groups when at least some words of the string of words of the phrase are included in words of a first permutation of the string of words of a first permutation of the phrase of the first entigen group. For example, the processing module identifies a substantial match when substituting pronouns of the phrase with pronouns of the first permutation of the phrase, i.e., substituting specific with generic names. As another example, the processing module identifies another substantial match when matching sequential verbs of the first permutation of the phrase i.e., “departed and cried”, “departed and was unhappy”. As yet another example, the processing module identifies the substantial match when matching object entigens of the phrase to those of the first permutation of the phrase i.e. the key person, a key place, etc.).

A second identification approach includes detecting that the first entigen group is associated with a first digital representation of a human reaction. For example, the processing module indicates the first digital representation of a human reaction when detecting that the first entigen group has the first digital representation of a human reaction to subsequently help establish the digital representation of a human reaction, i.e. sad).

A third identification approach includes matching the entigen group to a second entigen group of the other entigen groups when at least some entigens of the entigen group are included in the second entigen group. For example, the processing module finds that the same entigens matchup even though different identigens may have been selected for the most likely meaning, i.e., different languages have different words/identigens but share a common global meaning.

The fourth identification approach includes detecting that the second entigen group is associated with a second digital representation of a human reaction. For example, the processing module indicates that the second entigen group has the second digital representation of a human reaction to also subsequently help establish the digital representation of a human reaction, i.e. sad.

The method continues at step 874 where the processing module determines a dominant digital representation of a human reaction of the other entigen groups. The determining the dominant digital representation of a human reaction of the other entigen includes a variety of determination approaches.

A first determination approach includes determining the corresponding digital representation of a human reaction of at least some of the other entigen groups to produce a plurality of digital representations of human reaction(s). For example, the processing module extracts a corresponding digital representation of a human reaction from an entigen record of an entigen of another entigen group. As another example, the processing module identifies the digital representation of a human reaction of a digital representation of a human reaction characteristic entigen that is linked to an entigen of the other entigen group. As yet another example, the processing module detects another corresponding digital representation of a human reaction (e.g., utilizing inference) of additional entigens associated with another entigen group, i.e., Mary was unhappy for days after Bob departed, Mary was sad for days after Bob departed.

A second determination approach includes determining the dominant digital representation of a human reaction based on the majority of the plurality of digital representations of human reaction(s). For example, the processing module identifies a common digital representation of a human reaction of a simple majority of the plurality of digital representations of human reaction(s). For instance, the processing module identifies the sad digital representation of a human reaction when most of the plurality of digital representations of human reaction(s) indicate sad. As another example, the processing module determines the dominant digital representation of a human reaction based on digital representations of human reaction(s) of the plurality of the digital representations of human reaction(s), that have been generated within a recent common time frame, i.e., freshest the digital representation of a human reaction interpretations. As yet another example, the processing module generates the dominant digital representation of a human reaction by applying a weighting factor to each digital representation of a human reaction of the plurality of digital representations of human reaction(s) based on a number of entigens of the corresponding other entigen group that match entigens of the entigen group.

The method continues at step 876 where the processing module generates a digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction. The generating of the digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction includes a variety of generating approaches.

A first generating approach includes establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction. For example, the processing module makes the digital representation of a human reaction the same as the dominant digital representation of a human reaction based on one of the default setting, the quality level associated with the dominant digital representation of a human reaction, a desired digital representation of a human reaction establishment approach, a historical record, etc.

A second generating approach includes establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction and at least one other digital representation of a human reaction of a plurality of digital representations of human reaction(s) associated with the other entigen groups. For example, the processing module combines digital representations of human reactions to produce the digital representation of a human reaction. As another example, the processing module aggregates digital representations of a human reactions to establish the dominant digital representation of a human reaction as a list of aggregated digital representations of human reaction(s). For instance, the dominant digital representation of a human reaction includes a plurality of digital representations of human reactions, i.e., sad and unhappy, or happy and thrilled.

The method continues at step 878 where the processing module updates the entigen group using the digital representation of a human reaction to produce an updated entigen group for storage in the knowledge database. For example, the processing module modifies the entigen group to include a digital representation of a human reaction characteristic entigen that is linked to at least some of the entigens of the entigen group. For instance, a sad characteristic entigen is linked to the entigens of the entigen group. As another example, the processing module updates at least some of the entigens of the entigen group to indicate the digital representation of a human reaction. For instance, the sad characteristic is attached to each entigen record of the entigen group within the knowledge database.

Alternatively, or in addition to, the processing module outputs the digital representation of a human reaction to a requesting entity. For example, the processing module sends the digital representation of a human reaction to the requesting entity in response to a query from the requesting entity.

The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system 10 of FIG. 1 or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system 10, cause the one or more computing devices to perform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, text, graphics, audio, etc. any of which may generally be referred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. For some industries, an industry-accepted tolerance is less than one percent and, for other industries, the industry-accepted tolerance is 10 percent or more. Other examples of industry-accepted tolerance range from less than one percent to fifty percent. Industry-accepted tolerances correspond to, but are not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, thermal noise, dimensions, signaling errors, dropped packets, temperatures, pressures, material compositions, and/or performance metrics. Within an industry, tolerance variances of accepted tolerances may be more or less than a percentage level (e.g., dimension tolerance of less than +/−1%). Some relativity between items may range from a difference of less than a percentage level to a few percent. Other relativity between items may range from a difference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal 1 has a greater magnitude than signal 2, a favorable comparison may be achieved when the magnitude of signal 1 is greater than that of signal 2 or when the magnitude of signal 2 is less than that of signal 1. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship.

As may be used herein, one or more claims may include, in a specific form of this generic form, the phrase “at least one of a, b, and c” or of this generic form “at least one of a, b, or c”, with more or less elements than “a”, “b”, and “c”. In either phrasing, the phrases are to be interpreted identically. In particular, “at least one of a, b, and c” is equivalent to “at least one of a, b, or c” and shall mean a, b, and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and “b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, “processing circuitry”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, processing circuitry, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, processing circuitry, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, processing circuitry, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, processing circuitry and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, processing circuitry and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with one or more other routines. In addition, a flow diagram may include an “end” and/or “continue” indication. The “end” and/or “continue” indications reflect that the steps presented can end as described and shown or optionally be incorporated in or otherwise used in conjunction with one or more other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid-state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information.

While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations. 

What is claimed is:
 1. A method for execution by a computing device, the method comprises: selecting an entigen group from a knowledge database, wherein the entigen group is a most likely meaning of a phrase of a string of words, wherein an entigen of the entigen group corresponds to a selected identigen from a set of identigens regarding a word of the string of words, wherein the set of identigens represent different meanings of the word; identifying other entigen groups of alternate phrases having the most likely meaning from the knowledge database, wherein the alternate phrases are different permutations of the string of words; determining a dominant digital representation of a human reaction of the other entigen groups; and generating a digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction.
 2. The method of claim 1 further comprises at least one of: outputting the digital representation of a human reaction to a requesting entity; and updating the entigen group using the digital representation of a human reaction to produce an updated entigen group for storage in the knowledge database.
 3. The method of claim 1, wherein the selecting the entigen group from the knowledge database comprises at least one of: initiating a search of the knowledge database to identify the entigen group when a search timeframe has expired; receiving a digital representation of a human reaction identification request from a requesting entity; detecting that the entigen group does not include the digital representation of a human reaction; and detecting that the entigen group includes an incorrect digital representation of a human reaction.
 4. The method of claim 1, wherein the identifying the other entigen groups of alternate phrases having the most likely meaning from the knowledge database comprises at least one of: matching the entigen group to a first entigen group of the other entigen groups when at least some words of the string of words of the phrase are included in words of a first permutation of the string of words of a first permutation of the phrase of the first entigen group; detecting that the first entigen group is associated with a first digital representation of a human reaction; matching the entigen group to a second entigen group of the other entigen groups when at least some entigens of the entigen group are included in the second entigen group; and detecting that the second entigen group is associated with a second digital representation of a human reaction.
 5. The method of claim 1, wherein the determining the dominant digital representation of a human reaction of the other entigen groups comprises: determining a corresponding digital representation of a human reaction of at least some of the other entigen groups to produce a plurality of digital representations of human reactions; and determining the dominant digital representation of a human reaction based on a majority of the plurality of digital representations of human reactions.
 6. The method of claim 1, wherein the generating the digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction comprises at least one of: establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction; and establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction and at least one other digital representation of a human reaction of a plurality of digital representations of human reactions associated with the other entigen groups.
 7. A computing device of a computing system, the computing device comprises: an interface; a local memory; and a processing module operably coupled to the interface and the local memory, wherein the processing module functions to: select an entigen group from a knowledge database, wherein the entigen group is a most likely meaning of a phrase of a string of words, wherein an entigen of the entigen group corresponds to a selected identigen from a set of identigens regarding a word of the string of words, wherein the set of identigens represent different meanings of the word; identify other entigen groups of alternate phrases having the most likely meaning from the knowledge database, wherein the alternate phrases are different permutations of the string of words; determine a dominant digital representation of a human reaction of the other entigen groups; and generate a digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction.
 8. The computing device of claim 7, where the processing module further functions to: output, via the interface, the digital representation of a human reaction to a requesting entity; and update the entigen group using the digital representation of a human reaction to produce an updated entigen group for storage, via the interface, in the knowledge database.
 9. The computing device of claim 7, where the processing module functions to select the entigen group from the knowledge database by at least one of: initiating, via the interface, a search of the knowledge database to identify the entigen group when a search timeframe has expired; receiving, via the interface, a digital representation of a human reaction identification request from a requesting entity; detecting that the entigen group does not include the digital representation of a human reaction; and detecting that the entigen group includes an incorrect digital representation of a human reaction.
 10. The computing device of claim 7, where the processing module functions to identify the other entigen groups of alternate phrases having the most likely meaning from the knowledge database by at least one of: matching the entigen group to a first entigen group of the other entigen groups when at least some words of the string of words of the phrase are included in words of a first permutation of the string of words of a first permutation of the phrase of the first entigen group; detecting that the first entigen group is associated with a first digital representation of a human reaction; matching the entigen group to a second entigen group of the other entigen groups when at least some entigens of the entigen group are included in the second entigen group; and detecting that the second entigen group is associated with a second digital representation of a human reaction.
 11. The computing device of claim 7, where the processing module functions to determine the dominant digital representation of a human reaction of the other entigen groups by: determining a corresponding digital representation of a human reaction of at least some of the other entigen groups to produce a plurality of digital representations of human reactions; and determining the dominant digital representation of a human reaction based on a majority of the plurality of digital representations of human reactions.
 12. The computing device of claim 7, where the processing module functions to generate the digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction by at least one of: establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction; and establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction and at least one other digital representation of a human reaction of a plurality of digital representations of human reactions associated with the other entigen groups.
 13. A computer readable memory comprises: a first memory element that stores operational instructions that, when executed by a processing module, causes the processing module to: select an entigen group from a knowledge database, wherein the entigen group is a most likely meaning of a phrase of a string of words, wherein an entigen of the entigen group corresponds to a selected identigen from a set of identigens regarding a word of the string of words, wherein the set of identigens represent different meanings of the word; a second memory element that stores operational instructions that, when executed by the processing module, causes the processing module to: identify other entigen groups of alternate phrases having the most likely meaning from the knowledge database, wherein the alternate phrases are different permutations of the string of words; a third memory element that stores operational instructions that, when executed by the processing module, causes the processing module to: determine a dominant digital representation of a human reaction of the other entigen groups; and a fourth memory element that stores operational instructions that, when executed by the processing module, causes the processing module to: generate a digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction.
 14. The computer readable memory of claim 13 further comprises: a fifth memory element stores operational instructions that, when executed by the processing module, causes the processing module to: output the digital representation of a human reaction to a requesting entity; and update the entigen group using the digital representation of a human reaction to produce an updated entigen group for storage in the knowledge database.
 15. The computer readable memory of claim 13, wherein the processing module functions to execute the operational instructions stored by the first memory element to cause the processing module to select the entigen group from the knowledge database by one or more of: initiating a search of the knowledge database to identify the entigen group when a search timeframe has expired; receiving a digital representation of a human reaction identification request from a requesting entity; detecting that the entigen group does not include the digital representation of a human reaction; and detecting that the entigen group includes an incorrect digital representation of a human reaction.
 16. The computer readable memory of claim 13, wherein the processing module functions to execute the operational instructions stored by the second memory element to cause the processing module to identify the other entigen groups of alternate phrases having the most likely meaning from the knowledge database by one or more of: matching the entigen group to a first entigen group of the other entigen groups when at least some words of the string of words of the phrase are included in words of a first permutation of the string of words of a first permutation of the phrase of the first entigen group; detecting that the first entigen group is associated with a first digital representation of a human reaction; matching the entigen group to a second entigen group of the other entigen groups when at least some entigens of the entigen group are included in the second entigen group; and detecting that the second entigen group is associated with a second digital representation of a human reaction.
 17. The computer readable memory of claim 13, wherein the processing module functions to execute the operational instructions stored by the third memory element to cause the processing module to determine the dominant digital representation of a human reaction of the other entigen groups by: determining a corresponding digital representation of a human reaction of at least some of the other entigen groups to produce a plurality of digital representations of human reactions; and determining the dominant digital representation of a human reaction based on a majority of the plurality of digital representations of human reactions.
 18. The computer readable memory of claim 13, wherein the processing module functions to execute the operational instructions stored by the fourth memory element to cause the processing module to generate the digital representation of a human reaction for the entigen group based on the dominant digital representation of a human reaction by one or more of: establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction; and establishing the digital representation of a human reaction to include the dominant digital representation of a human reaction and at least one other digital representation of a human reaction of a plurality of digital representations of human reactions associated with the other entigen groups. 